A file retrieval method and system based on AI images and key talent information

Abstract

The present invention proposes an archive retrieval method and system based on AI images and key information of talents. The method includes collecting archive data and performing preprocessing and feature extraction, performing multimodal fusion on features to obtain multimodal features, and mapping them into a unified feature space; using archive features to generate high-order features, combining high-order features of different modes to generate a global multimodal feature representation; repairing defective parts in archive images and outputting the repaired image, and then dynamically enhancing it; converting the final multimodal feature representation into a quantum state, performing amplitude amplification and search, extracting the most matching features and decoding them; extracting user preferences, optimizing the sorting of retrieval results, optimizing the model and recommending personalized services to users; dynamically adjusting the system state, optimizing the state space model to obtain the optimal behavior strategy, constraining the optimal behavior strategy, dynamically adjusting the multimodal features, and performing incremental updates of the system.

Description

Technical Field

[0001] The present invention belongs to the field of archive retrieval, and in particular to an archive retrieval method and system based on AI images and talent key information. Background Art

[0002] With the development of information technology, electronic archives have gradually replaced the traditional paper archive management method, and various organizations, government departments and enterprises have accumulated a large number of electronic archives. These archives usually contain multiple types of data such as text, images, audio, and video. However, with the rapid increase in the number of archives and the diversification of data types, traditional archive management systems have gradually exposed a series of problems. Existing archive retrieval systems usually rely on simple keyword matching or queries based on specific fields, which have obvious limitations when processing large-scale, multimodal (such as text and image combination) archival data. First, traditional retrieval methods are insufficient in the integration of multimodal data, and cannot effectively perform unified feature extraction and comprehensive analysis of multiple data forms such as text, images, and audio, resulting in low accuracy and relevance of retrieval results.

[0003] In addition, many archives are often blurred, damaged, distorted, etc. due to long storage time, aging media or improper preservation, especially in the process of digitization of paper archives, which further increases the difficulty of retrieval. Although existing image processing technology can repair or enhance image quality to a certain extent, its effect is still limited when facing severely damaged or missing archival images. In addition, with the continuous expansion of the scale of archives, the retrieval efficiency of the system when processing large-scale data has also been challenged. Traditional computing methods and hardware architectures often cannot meet the needs of real-time response when processing hundreds of millions of archives, resulting in poor user experience. Especially in government and large enterprises, the management and utilization efficiency of archival data is of great significance for decision support, preservation and retrieval of historical records. Therefore, it is difficult for existing technologies to effectively respond to these challenges, and new solutions are urgently needed to improve the performance of archival retrieval systems. Summary of the invention

[0004] The present invention provides an efficient and intelligent archive retrieval system by introducing multimodal deep learning, image restoration and enhancement technology, and quantum computing acceleration algorithm. It successfully overcomes the problems of insufficient multimodal data integration capabilities and retrieval difficulties caused by damage to historical archive images, and provides a new solution for the field of archive management and retrieval, significantly improving the system's intelligence level, retrieval accuracy, and processing efficiency.

[0005] In order to achieve the above-mentioned object, a first aspect of the present invention provides a file retrieval method based on AI images and key information of talents, the method comprising:

[0006] S1. Collecting archival data and performing preprocessing and feature extraction, wherein the archival data includes text data, image data and audio data, and performing multimodal fusion on the extracted archival features to obtain multimodal features, and mapping the multimodal features to a unified feature space through a weighted fusion method; wherein the archival features include text features, image features and audio features;

[0007] S2. Construct an extraction method based on a joint multimodal convolutional network to extract multimodal features from archive features to generate high-order features, and use dynamic weighted fusion to combine high-order features of different modalities to generate a global multimodal feature representation;

[0008] S3. Design a generative adversarial network based on multi-resolution residuals to repair blurred, damaged or missing parts in archival images, conduct adversarial training guided by multimodal features to output the repaired images, and design a post-processing module based on adaptive contrast enhancement to dynamically enhance the repaired images;

[0009] S4. Convert the final multimodal feature representation into a quantum state, use quantum amplitude amplification technology based on Grover algorithm to amplify and search the quantum state, extract the best matching feature through quantum measurement according to the amplification and search results, and decode the best matching feature into classical information;

[0010] S5. Design a user behavior model based on dynamic adjustment of multimodal feature weights to extract user preferences. Design a personalized retrieval model based on weighted interaction to optimize the ranking of retrieval results according to user preferences. Then design an adaptive optimization mechanism based on feedback loops to optimize user preferences and personalized retrieval models by analyzing user feedback when using the system. Then design a recommendation algorithm based on user preferences and profile features to calculate the matching degree between user behavior features and profile data for personalized recommendation services.

[0011] S6. Design a state-space model to dynamically adjust the system state. Use a reinforcement learning algorithm based on Q-learning to optimize the state-space model and learn the optimal behavior strategy. At the same time, introduce a regularization term based on domain knowledge into the optimal behavior strategy to constrain the optimal behavior strategy in a specific domain. Dynamically adjust the multimodal features based on real-time user feedback and environmental changes, and design a dynamic feedback mechanism based on incremental learning to perform incremental updates on the system.

[0012] Furthermore, when extracting text features, based on the fact that the text data in the scene contains key terms in a specific field, domain-specific regularization terms are introduced to strengthen the expression of specific terms; when extracting image features, processing terms based on gradient regularization are introduced to constrain the gradient changes of the image; when extracting audio features, regularization terms based on spectrum smoothing are introduced to constrain the high-frequency noise in the spectrum;

[0013] The weighted fusion method is:

[0014] Unify the features of each modality into the shared feature space M through linear mapping unified , the specific formula is as follows:

[0015]

[0016] Among them, W T , W I , W A are the linear mapping matrices of text, image, and audio features, respectively, T final ,I final , A final are the feature representations of text, image, and audio features respectively. is the regularization term used to balance the characteristics of different modes, λ fusi on is the weight of the fusion regularization term, and the fusion regularization term is introduced Form the final multimodal feature representation M final , which is expressed as follows:

[0017]

[0018] Among them, λ balance is the regularization coefficient.

[0019] Furthermore, the extraction method based on the joint multimodal convolutional network specifically comprises the following steps:

[0020] For each modality of data, design a joint multimodal convolution kernel W m , acting on the feature representation of each modality to generate a high-order feature map F m , which is expressed as follows:

[0021] F m =σ(W m ·T final )+σ(W m I final )+σ(W m ·A final )

[0022] Among them, σ(·) is a nonlinear activation function, W m is a multimodal convolution kernel;

[0023] The dynamic weighted fusion is expressed as follows:

[0024]

[0025] Among them, α m is the weight of each modal feature, F m is the high-order feature map of the mth mode, M fusion Represents weighted fusion of multimodal features;

[0026] At the same time, a mechanism based on entropy regularization is introduced in the weighted fusion of multimodal features. Constraining a certain modal feature to be overly dominant in the fusion process, the final multimodal feature representation M final It is expressed as follows:

[0027]

[0028] Among them, λ fusion is the regularization coefficient, To regularize the fused feature entropy.

[0029] Furthermore, the multi-resolution residual-based generative adversarial network includes a generator and a discriminator, both of which are optimized and image repaired through adversarial training;

[0030] The design of the generator network is based on a multi-resolution residual module, and its core operation formula is expressed as follows:

[0031]

[0032] Among them, G(·) is the generator network, X damaged is the input damaged image, X repaired is the output repaired image, L is the number of layers of the multi-resolution residual module, and W l is the convolution weight matrix of the lth layer, ResBlock l (·) is the residual module of the lth layer;

[0033] The output of the discriminator network is expressed as follows:

[0034] D(X,M final )=σ(W d ·[X,M final ]+b d )

[0035] Where D(·) is the discriminator network, X is the input image, and M final is the multimodal fusion feature obtained from the first two steps, W d is the weight matrix of the discriminator, b dis the bias term, σ(·) is the activation function;

[0036] The loss function of the generator is expressed as follows:

[0037]

[0038] Among them, λ structure is the weight coefficient of the regularization term, is a structural regularization term used to constrain the local structure of the image.

[0039] Furthermore, the final multimodal feature representation M final Mapped to the quantum state, let the final multimodal characteristics Map it to the quantum state ψ M , which is expressed as follows:

[0040]

[0041] Among them, M i is the feature vector M final The i-th component of final ∥ is the Euclidean norm of the eigenvector, e i is the quantum state basis vector, d is the d vector;

[0042] When encoding quantum states, a regularization term based on feature sparsity is introduced Optimizing the quantum state ψ M The expression is as follows:

[0043]

[0044] Furthermore, the quantum amplitude amplification technology based on the Grover algorithm specifically includes:

[0045] Denote the quantum state of the target feature as ψ target , the quantum state in the database is represented by ψ M , through the quantum operator of Grover's algorithm For the initial quantum state ψ M Amplify the amplitude so that the final quantum state ψ search and the target quantum state ψ target The overlap probability is maximized; wherein the Grover algorithm is expressed as follows:

[0046]

[0047] Among them, ψ target is the quantum state representation of the target feature, ψ M is the quantum state representation in the database, is the quantum operator of Grover's algorithm, <ψM |ψ target > is the overlap between the target state and the database state;

[0048] Introducing a weighting mechanism based on multimodal information entropy into the Grover algorithm The quantum state is weighted according to the information entropy of the multimodal features, so that the feature with the highest information content has the largest weight in the search, which is expressed as follows:

[0049]

[0050] Furthermore, the best matching feature is extracted by quantum measurement, and the quantum state ψ is calculated. search The ground state e with the highest probability amplitude i Determine the best matching feature, where the quantum state ψ search The probability calculation formula of the measurement is expressed as follows:

[0051] P(i)=| <e i |ψ search >| 2

[0052] Where P(i) is the measurement result for the ground state e i The probability of <e i |ψ search > is the quantum state ψ search In the ground state i The projection amplitude on ;

[0053] At the same time in the quantum state ψ search The probability calculation formula of the measurement introduces a regularization term based on the measurement error Reduce the impact of quantum noise, expressed as follows:

[0054]

[0055] Furthermore, the user behavior model based on dynamic adjustment of multimodal feature weights is expressed as follows:

[0056]

[0057] Among them, U t is the user behavior feature, represents the user preference, n represents the total number of users, m represents the total number of multimodal features, B t,i is the i-th user behavior data at time point t, β i is the weight of the corresponding behavior data, is a regularization function based on time decay and behavior correlation, M final,j is the jth multimodal feature, γ j is the dynamic adjustment coefficient of multimodal features, λmulti is the fusion weight of multimodal features and user behavior features;

[0058] The personalized retrieval model calculates the relevance score of each archive by matching the user behavior characteristics and the archive multimodal characteristics, and sorts the retrieval results according to the score, wherein the score is calculated as follows:

[0059]

[0060] Among them, S i is the personalized score of the i-th profile, W s is the sorting weight matrix, M i is the multimodal feature vector of the ith archive, U t is the user behavior feature vector, ⊙ is the weighted interaction operation of the feature, is the context regularization term, λ context is its weight;

[0061] And introduce a regularization term based on content diversity in the score calculation formula To constrain the search content, it is expressed as follows:

[0062]

[0063] Among them, sim(M i ,M j ) is the feature similarity between files i and j, Relevance(S i ,S j ) is their relevance in the sorting.

[0064] Furthermore, the adaptive learning formula for state updating of the state space model is expressed as follows:

[0065]

[0066] Among them, W s , W a , W f are the weight matrices of system status, behavior strategy and user feedback, respectively, adapt is the weight of the adaptive regularization term, is an adaptive regularization term used to balance the relationship between the current state, behavior strategy, and user feedback.

[0067] In a second aspect of the present invention, there is provided an archive retrieval system based on AI images and key information of talents, the system comprising:

[0068] A data acquisition module, used to collect archive data and perform preprocessing and feature extraction, wherein the archive data includes text data, image data and audio data, and to perform multimodal fusion on the extracted archive features to obtain multimodal features, and to map the multimodal features into a unified feature space through a weighted fusion method; wherein the archive features include text features, image features and audio features;

[0069] The feature extraction and fusion module is used to construct an extraction method based on a joint multimodal convolutional network to extract multimodal features from archive features to generate high-order features, and use dynamic weighted fusion to combine high-order features of different modalities to generate a global multimodal feature representation;

[0070] The image restoration and enhancement module is used to design a generative adversarial network based on multi-resolution residuals to restore blurred, damaged or missing parts in archival images, and conduct adversarial training guided by multimodal features to output the restored images. A post-processing module based on adaptive contrast enhancement is designed to dynamically enhance the restored images.

[0071] The quantum computing acceleration retrieval module is used to convert the final multimodal feature representation into a quantum state, and use the quantum amplitude amplification technology based on the Grover algorithm to amplify and search the quantum state. According to the amplification and search results, the best matching features are extracted through quantum measurement, and the best matching features are decoded into classical information;

[0072] The personalized intelligent retrieval and optimization module is used to design a user behavior model based on dynamic adjustment of multimodal feature weights to extract user preferences, design a personalized retrieval model based on weighted interaction according to user preferences to optimize the ranking of retrieval results, and then design an adaptive optimization mechanism based on feedback loops to optimize user preferences and personalized retrieval models by analyzing user feedback behaviors when using the system. Then, a recommendation algorithm based on user preferences and profile features is designed to calculate the matching degree between user behavior features and profile data for personalized recommendation services.

[0073] The system adaptive learning and optimization module is used to design a state-space model to dynamically adjust the system state. It uses a reinforcement learning algorithm based on Q-learning to optimize the state-space model and learn the optimal behavior strategy. At the same time, it introduces a regularization term based on domain knowledge into the optimal behavior strategy to constrain the optimal behavior strategy in a specific field. It dynamically adjusts the multimodal features according to the user's real-time feedback and environmental changes, and designs a dynamic feedback mechanism based on incremental learning to perform incremental updates on the system.

[0074] The beneficial technical effects of the present invention are at least as follows:

[0075] (1) To address the problem of insufficient multimodal data integration capabilities, the present invention uses an advanced multimodal deep learning model to uniformly encode and learn image, text, audio and other data, and achieve deep fusion and correlation analysis between different data types. Through this multimodal processing method, the system can combine multiple data sources when processing complex queries to generate more accurate and relevant search results.

[0076] (2) This invention introduces generative adversarial network technology to automatically repair and enhance damaged archival images. This technology can not only effectively repair blurred and damaged images, but also improve the clarity and recognizability of images, thereby ensuring the integrity and accuracy of image data during the retrieval process.

[0077] (3) The present invention adopts quantum computing technology and significantly accelerates the classification and matching process of high-dimensional data through quantum algorithms such as quantum support vector machines, so that the system can still maintain a high response speed when facing massive data and meet the needs of real-time retrieval. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0079] Figure 1 This is a flow chart of an archive retrieval method based on AI images and key talent information of the present invention.

[0080] Figure 2 This is a framework diagram of an archive retrieval system based on AI images and key talent information of the present invention. DETAILED DESCRIPTION

[0081] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0082] like Figure 1 As shown, an embodiment of the present invention provides an intelligent monitoring method for a power distribution cabinet, the method comprising:

[0083] S1. Collect archival data and perform preprocessing and feature extraction, wherein the archival data includes text data, image data and audio data, and perform multimodal fusion on the extracted archival features to obtain multimodal features, and map the multimodal features to a unified feature space through a weighted fusion method; wherein the archival features include text features, image features and audio features.

[0084] Specifically, in S1, the specific steps are:

[0085] S101, preprocessing and feature extraction of text data, the specific method is as follows:

[0086] For the text data in the archives (such as resumes, reports, etc.), the context-aware text encoding method is used to process the text data in the archives. The text is first processed by word segmentation and annotation to obtain the input sequence In order to capture the semantic and contextual relationships in the text, an adaptive attention mechanism is designed to dynamically adjust the weight of each word according to its importance in the context. The formula of the adaptive attention mechanism is expressed as follows:

[0087]

[0088] in, Words The embedding representation of q i Words The query vector is Words The key vector, α ij is the attention weight;

[0089] On this basis, considering that the text data in the patent scenario may contain key terms in a specific field, we introduce a field-specific regularization term Used to strengthen the expression of specific terms. The global features of the text are expressed as:

[0090]

[0091] Among them, β i is the vocabulary importance coefficient after weighting by the attention mechanism, λ text is the weight of the regularization term, A regularization method for strengthening domain key terms;

[0092] S102: Preprocess and extract features of the image data. The specific method is as follows:

[0093] In view of the complex visual information contained in the image data in the archives (such as scanned documents, photos, etc.), a multi-scale convolutional neural network is designed to extract image features. I After normalization, a series of multi-scale convolution kernels W m Extract its multi-level features F I,m , design an adaptive nonlinear feature extraction scheme, which is expressed as follows:

[0094]

[0095] Among them, γ m is the adaptive weight of the convolution kernel, σ(·) is the nonlinear activation function, b m is a bias term. In this way, the multi-level features of the image are effectively extracted and enhanced, and the obtained image global feature representation I can capture the important visual information in the image.

[0096] In addition, in order to reduce the noise and overfitting in the image features, a processing term based on gradient regularization is introduced To constrain the gradient change of the image, the final image features are expressed as follows:

[0097]

[0098] Among them, λ image is the regularization weight, By suppressing gradient fluctuations to reduce noise in image features, this method ensures the stability and accuracy of image features and provides reliable image features for subsequent processing;

[0099] S103: Preprocess and extract features of the audio data. The specific method is as follows:

[0100] For the audio data in the archives (such as interview recordings, voice records, etc.), a time-frequency domain convolutional network is used to process the time-varying and frequency-domain characteristics of the audio data. First, the audio signal is converted into a spectrum representation A through short-time Fourier transform. STFT , design a multi-layer time-frequency convolutional network to extract the global features of audio, expressed as follows:

[0101] A=TF-CNN(A STFT )

[0102] Among them, TF-CNN contains multiple convolutional layers, which capture different frequency and time features in the spectrogram respectively. The network can effectively extract key information in the audio and generate a global audio feature vector A;

[0103] To address the noise problem of audio data, a regularization term based on spectrum smoothing is designed To reduce the high-frequency noise in the spectrum, the final audio features are expressed as follows:

[0104]

[0105] Among them, λ audio is the regularization weight, The spectral noise is reduced through smoothing to ensure the clarity and consistency of audio features. This process ensures the reliability and effectiveness of audio features and provides high-quality audio information for multimodal fusion.

[0106] S104. Design a weighted fusion strategy to unify the features of each modality into the shared feature space M through linear mapping. unified , the specific formula is as follows:

[0107]

[0108] Among them, W T , W I , W A are the linear mapping matrices of text, image, and audio features, respectively, T final ,I final , A final are the feature representations of text, image, and audio features respectively. is the regularization term used to balance the characteristics of different modes, λ fusi on is the weight of the fusion regularization term, and the fusion regularization term is introduced It is used to constrain the relative importance of different modal features, ensure the balance and rationality of feature fusion, and form the final multimodal feature representation M final , which is expressed as follows:

[0109]

[0110] Among them, λ balance is the regularization coefficient. It ensures the fusion effect of multimodal features.

[0111] S2. Construct an extraction method based on a joint multimodal convolutional network to perform multimodal feature extraction on archive features to generate high-order features, and use dynamic weighted fusion to combine high-order features of different modalities to generate a global multimodal feature representation.

[0112] For multimodal data, an innovative feature extraction and fusion scheme is designed so that the features of each modality can be effectively integrated in a unified feature space and provide support for subsequent retrieval and analysis.

[0113] Specifically, in S2, the specific steps are:

[0114] S201. Based on the previous steps, the text, image, and audio data have generated preliminary feature representations T through their respective network structures (such as adaptive attention mechanism, convolutional neural network, time-frequency domain convolutional network). final ,I final and A final To further extract high-order information from these features, we introduce an extraction method based on a joint multimodal convolutional network.

[0115] Furthermore, for each modality of data, a joint multimodal convolution kernel W is designed m, and apply it to the feature representation of each modality. After the features of each modality are jointly convolved, a high-order feature map F is generated. m , which is expressed as follows:

[0116] F m =σ(W m ·T final )+σ(W m I final )+σ(W m ·A final )

[0117] Among them, σ(·) is a nonlinear activation function, W m is a multimodal convolution kernel, which is used to map each modal feature into a unified high-dimensional space F m ,This operation not only extracts high-order information in each modality, but also effectively integrates the interaction features between different modalities;

[0118] S202. Design a weighted feature fusion mechanism to combine high-order features of different modalities through dynamic weighting to generate a global multimodal feature representation M. fusion , the specific weighted fusion strategy is expressed as follows:

[0119]

[0120] Among them, α m It is dynamically adjusted through an adaptive attention mechanism to ensure that during the fusion process, weights can be reasonably allocated according to the importance of different modal features; F m is the high-order feature map of the mth mode, M fusion Represents weighted fusion of multimodal features;

[0121] S203. Introducing a mechanism based on entropy regularization To prevent a certain modality feature from being overly dominant during the fusion process, which may lead to deviations in the fusion results, the final features after fusion are expressed as follows:

[0122]

[0123] Among them, λ fusion is the regularization coefficient, In order to regularize the fused feature entropy, the balance of different modalities is ensured during the fusion process.

[0124] S3. Design a generative adversarial network based on multi-resolution residuals to repair blurred, damaged or missing parts in archival images, and conduct adversarial training guided by multimodal features to output the repaired images. Design a post-processing module based on adaptive contrast enhancement to dynamically enhance the repaired images.

[0125] Since the image data in the archives may be damaged, blurred or missing information due to historical reasons, simple traditional image processing methods are difficult to meet the needs of high-quality restoration. Therefore, an innovative multi-resolution residual generative adversarial network is designed to deal with these problems. The restored image not only has higher visual quality, but also maintains consistency with the original data, providing strong support for multimodal fusion and analysis in subsequent steps.

[0126] Specifically, the specific steps of S3 include:

[0127] S301. A generative adversarial network based on multi-resolution residuals is used to repair blurred, damaged or missing parts in archival images. The network includes a generator and a discriminator, which are optimized through adversarial training to generate high-quality repaired images.

[0128] Specifically, the design of the generator network is based on a multi-resolution residual module, which can extract detailed information in the image at different resolution levels, so as to better repair blurred or damaged areas. The core operation formula of the generator is expressed as follows:

[0129]

[0130] Among them, G(·) is the generator network, X damaged is the input damaged image, X repaired is the output repaired image, L is the number of layers of the multi-resolution residual module, and W l is the convolution weight matrix of the lth layer, ResBlock l (·) is the residual module of the lth layer. The residual module retains the high-frequency detail information of the image by directly connecting the input and output, so that the network can maintain the integrity of the original structure while repairing the image;

[0131] S302. In order to ensure that the visual quality of the generated image is as close as possible to the real image, a feature-guided discriminator network is designed. The discriminator not only considers the visual features of the image, but also combines multimodal feature information to evaluate the generated image. The output of the discriminator network is expressed as follows:

[0132] D(X,M final )=σ(W d ·[X,M final ]+b d )

[0133] Where D(·) is the discriminator network, X is the input image (including real images and generated images), M finalis the multimodal fusion feature obtained from the first two steps, W d is the weight matrix of the discriminator, b d is the bias term, and σ(·) is the activation function. Through this design, the discriminator not only judges the authenticity of the image based on visual quality, but also uses multimodal features to provide additional contextual information, thereby guiding the generator optimization more accurately.

[0134] The discriminator network will evaluate the real images and generated images during the training process, and guide the generator to continuously optimize the generated results by comparing the features with the real images. The goal of the generator is to make the discriminator unable to distinguish between the generated images and the real images, so the training between the generator and the discriminator is an adversarial process;

[0135] S303: In order to ensure that the image restoration result is structurally consistent with the original image and has high reliability in specific fields, a structural regularization term is introduced. The local structure of the image is constrained so that the restored image is not only visually close to the real image, but also retains the key structural information of the original image. The loss function of the generator is expressed as follows:

[0136]

[0137] Among them, λ structure is the weight coefficient of the regularization term, It is a structural regularization term, and its main function is to ensure that the quality of the restored image meets the domain characteristics of the archival data by constraining the structural information of the restored image, such as edge and texture features. This structural regularization not only enhances the authenticity of the generated image, but also prevents the image from being over-smoothed or structurally distorted during the restoration process;

[0138] S304. In order to further improve the visual quality and recognizability of the image, a post-processing module based on adaptive contrast enhancement is designed to further improve the visual quality and recognizability of the image. This module can dynamically adjust the brightness and contrast of the generated image so that it has a better visual effect on the display device while retaining important detail information.

[0139] Specifically, the operation formula of the adaptive contrast enhancement module is expressed as follows:

[0140]

[0141] Among them, X enhanced is the image after enhancement processing, X repaired is the restored image output by the generator, α is the contrast enhancement coefficient, is the mean of the repaired image. Through this adaptive contrast adjustment, the detail expression of the image can be effectively improved, especially in the low-contrast area, making the image more usable in subsequent multimodal feature extraction and retrieval.

[0142] S4. Convert the final multimodal feature representation into a quantum state, use quantum amplitude amplification technology based on Grover algorithm to amplify and search the quantum state, extract the most matching features through quantum measurement according to the results of amplification and search, and decode the most matching features into classical information.

[0143] Given that a unified high-dimensional multimodal feature space has been established in the previous steps and image restoration and enhancement has been performed, the current task is how to quickly and accurately retrieve matching archival data in this high-dimensional space. Traditional retrieval methods are limited in efficiency and accuracy when faced with massive amounts of data, so we designed an efficient retrieval solution based on quantum computing, which is achieved through steps such as quantum state encoding, multimodal feature mapping, Grover algorithm accelerated search, and quantum state measurement.

[0144] Specifically, the specific steps of S4 include:

[0145] S401, it is necessary to represent the multimodal features obtained in the previous step M final Converted to quantum state for processing in quantum computing framework. Given a multimodal feature vector We map this to the quantum state ψ by M , which is expressed as follows:

[0146]

[0147] Among them, M i is the feature vector M final The i-th component of final ∥ is the Euclidean norm of the eigenvector, e i is the quantum state basis vector, d is the d vector;

[0148] Considering that the characteristics of archival data often contain noise and redundant information, a regularization term based on feature sparsity is introduced in quantum state encoding. Optimizing the quantum state ψ M This term is used to reduce the complexity of high-dimensional features, thereby optimizing the representation of quantum states. The formula is as follows:

[0149]

[0150] S402, quantum amplitude amplification technology based on Grover algorithm quickly searches for the quantum state that best matches the target feature, which is used to quickly search for the quantum state that best matches the target feature in the high-dimensional quantum state space, specifically including:

[0151] Denote the quantum state of the target feature as ψ target , the quantum state in the database is represented by ψ M , through the quantum operator of Grover's algorithm For the initial quantum state ψ M Amplify the amplitude so that the final quantum state ψ search and the target quantum state ψ target The overlap probability is maximized; wherein the Grover algorithm is expressed as follows:

[0152]

[0153] Among them, ψ target is the quantum state representation of the target feature, ψ M is the quantum state representation in the database, is the quantum operator of Grover's algorithm, <ψ M |ψ target > is the overlap between the target state and the database state; through this operation, the Grover algorithm can quickly amplify the probability amplitude of the most matching quantum state, thereby significantly accelerating the feature matching search process;

[0154] Introducing a weighting mechanism based on multimodal information entropy into the Grover algorithm The quantum state is weighted according to the information entropy of the multimodal features, so that the feature with the highest information content has the largest weight in the search, which is expressed as follows:

[0155]

[0156] S403. After the quantum amplitude amplification and search are completed, the next step is to extract the best matching features through quantum measurement and decode them into classical information for subsequent use. The goal of quantum measurement is to determine the search Which ground state e i With the highest probability amplitude. The best matching feature is extracted by quantum measurement, and the quantum state ψ is calculated search The ground state e with the highest probability amplitude i Determine the best matching feature, where the quantum state ψ search The probability calculation formula of the measurement is expressed as follows:

[0157] P(i)=| <e i |ψ search >| 2

[0158] Where P(i) is the measurement result for the ground state e i The probability of <e i |ψ search > is the quantum state ψ search In the ground state i The projection amplitude on ;

[0159] At the same time in the quantum state ψ search The probability calculation formula of the measurement introduces a regularization term based on the measurement error Reduce the impact of quantum noise, expressed as follows:

[0160]

[0161] S5. Design a user behavior model based on dynamic adjustment of multimodal feature weights to extract user preferences. Design a personalized retrieval model based on weighted interaction according to user preferences to optimize the ranking of retrieval results. Then design an adaptive optimization mechanism based on feedback loops to optimize user preferences and personalized retrieval models by analyzing user feedback behavior when using the system. Next, design a recommendation algorithm based on user preferences and profile features to calculate the matching degree between user behavior features and profile data for personalized recommendation services.

[0162] When building a personalized retrieval system, we first need to fine-tune the user's behavior model to capture their search preferences and behavior patterns. t The generation of is based on its behavioral data B at different time points t t . These data include information such as users’ search history, click behavior, dwell time, and preferred categories.

[0163] Specifically, the specific steps of S5 include:

[0164] S501: Construct a user behavior model based on dynamic adjustment of multimodal feature weights, as shown below:

[0165]

[0166] Among them, B t,i is the i-th user behavior data at time point t, β i is the weight of the corresponding behavior data, is a regularization function based on time decay and behavior correlation, used to adjust the behavior weight, M final,j is the jth multimodal feature, γ j is the dynamic adjustment coefficient of multimodal features, λ multiis the fusion weight of multimodal features and user behavior features. In this way, the user's behavior features not only reflect the current behavior preferences, but also integrate the multimodal features generated in the previous steps, ensuring that the retrieval system can comprehensively consider the user's personalized needs.

[0167] Furthermore, when obtaining the user behavior feature vector U t After that, the next step is to apply it to the personalized retrieval model to optimize the ranking of retrieval results. The core of this model is to calculate the relevance score of each archive by matching user behavior characteristics and archive multimodal features, and sort the retrieval results according to the score.

[0168] The personalized retrieval model calculates the relevance score of each archive by matching the user behavior characteristics and the archive multimodal characteristics, and sorts the retrieval results according to the score, wherein the score is calculated as follows:

[0169]

[0170] Among them, S i is the personalized score of the i-th profile, W s is the sorting weight matrix, M i is the multimodal feature vector of the ith archive, U t is the user behavior feature vector, ⊙ is the weighted interaction operation of the feature, is the context regularization term, λ context is its weight; the context regularization term is used to introduce search context information, such as the user's current query intention, usage scenario, etc., to ensure that the returned results are highly relevant to the user's current needs.

[0171] Introducing a regularization term based on content diversity into the score calculation formula To constrain the search content, it is expressed as follows:

[0172]

[0173] Among them, sim(M i ,M j ) is the feature similarity between files i and j, Relevance(S i ,S j ) is their relevance in the ranking. By rewarding results with low similarity and high relevance, the system is able to provide a more diverse set of results.

[0174] S502: Analyze the user's feedback behavior when using the system (such as click behavior, bounce rate, dwell time, etc.), and generate the user behavior feature vector U t And the retrieval model is continuously optimized, and a feedback optimization formula based on incremental learning is introduced, which is expressed as follows:

[0175]

[0176] Among them, α is the coefficient of controlling feedback balance, Based on the new feedback data B t+1 The feedback regularization term is used to update user behavior features. Through this dynamic adjustment, the system can optimize the retrieval model according to the user's real-time feedback, so that the model can be updated adaptively and continuously improve the accuracy and efficiency of personalized retrieval.

[0177] S503. Design a recommendation algorithm based on user behavior and profile features, generate a recommendation list by calculating the matching degree between user behavior features and profile data, and push personalized information. The formula of the recommendation model is as follows:

[0178]

[0179] Among them, R j is the score of the jth recommended profile, W r is the recommendation weight matrix, M j is the multimodal feature vector of the jth archive, U t is the user behavior feature vector, λ personal is the weight of the personalized regularization term, It is a personalized measurement function based on user behavior characteristics. The recommendation algorithm generates a recommended list of profiles that matches user needs and pushes it in real time by analyzing user behavior characteristics and preferences.

[0180] S504. Design a joint optimization mechanism of multimodal features and user preferences to generate a more personalized multimodal feature representation M by combining user behavior characteristics in the multimodal feature extraction process. personal , the specific formula is:

[0181]

[0182] Among them, M final is the multimodal feature vector generated in the previous step, λ user is the weight of the user behavior feature, It is a joint regularization term used to adjust the correlation between multimodal features and user behavior features. This mechanism can dynamically optimize multimodal features, making the system more adaptable and flexible in personalized retrieval and recommendation.

[0183] S6. Design a state-space model to dynamically adjust the system state. Use a reinforcement learning algorithm based on Q-learning to optimize the state-space model and learn the optimal behavior strategy. At the same time, introduce a regularization term based on domain knowledge into the optimal behavior strategy to constrain the optimal behavior strategy in a specific domain. Dynamically adjust the multimodal features based on real-time user feedback and environmental changes, and design a dynamic feedback mechanism based on incremental learning to perform incremental updates on the system.

[0184] Specifically, the specific steps of S6 include:

[0185] S601. Design a state space model to combine the current state of the system, user behavior, and environmental feedback, and an adaptive learning formula for state update of the state space model. Define the state vector of the system at time t as S t , the system's behavior strategy is A t , the user's real-time feedback is F t , then the adaptive learning formula for state update is expressed as follows:

[0186]

[0187] Among them, W s , W a , W f are the weight matrices of system status, behavior strategy and user feedback, respectively, adapt is the weight of the adaptive regularization term, is an adaptive regularization term used to balance the relationship between the current state, behavior strategy, and user feedback.

[0188] Specifically, this mechanism enables the system to make adaptive adjustments based on user behavior feedback and the current system status. For example, when user behavior changes significantly, the system can respond quickly and update its strategy to continue to meet user needs.

[0189] S602. A reinforcement learning algorithm based on Q learning is adopted. The system evaluates the value of different strategies in the state space and selects the strategy that can maximize the long-term benefits for execution. The core of Q learning lies in the iterative update of the Q function, which is specifically expressed as follows:

[0190]

[0191] Among them, α is the learning rate, R t is the immediate return, γ is the discount factor, and A′ is the t+1 Q learning is a process of continuous iterative updating, which enables the system to gradually learn the optimal behavior strategy to be adopted under different states.

[0192] In this process, a regularization term based on domain knowledge is introduced It is used to guide the system to consider the constraints and goals of a specific domain when choosing a behavior strategy, as shown below:

[0193]

[0194] in, is the original immediate return, λ domain is the weight of the domain regularization term, It is used to constrain the behavior of the system so that the system can meet the needs and specifications of specific fields while pursuing benefits. This improvement ensures that the system can be more stable and reliable in practical applications.

[0195] S603. Introduce a collaborative optimization mechanism to dynamically adjust multimodal features based on real-time user feedback and environmental changes. The goal of collaborative optimization is to make full use of the unique information of each modality while maintaining the overall consistency of the features. The core formula of collaborative optimization is expressed as follows:

[0196]

[0197] Among them, M opt is the optimized multimodal feature representation, λ co is the weight coefficient of collaborative optimization, It is a collaborative regularization term used to adjust the matching degree between multimodal features and user behaviors. This adjustment ensures that the system can adaptively select and optimize features under different users and different environments, thereby improving the overall retrieval effect and system performance.

[0198] S604. Design a dynamic feedback mechanism based on incremental learning, allowing the system to receive and process real-time feedback from users during operation, and perform incremental updates of the model based on these feedbacks. The basic formula of incremental learning is expressed as follows:

[0199]

[0200] Among them, θ t is the parameter of the model at time t, η is the learning rate, is the current feedback F t For the loss function Through incremental updates, the system can gradually adjust parameters to adapt to new data and feedback without completely retraining the model.

[0201] In order to further improve the stability of incremental learning, a regularization term based on time decay is introduced It is used to control the update amplitude to prevent the model from changing drastically due to short-term fluctuations. The specific expression is as follows:

[0202]

[0203] in, is a time-decay regularization term, which gradually decreases with the increase of time t to ensure the stability and consistency of model updating.

[0204] like Figure 2 As shown, an embodiment of the present invention further provides an intelligent monitoring system for a power distribution cabinet, the system comprising:

[0205] The data acquisition module 10 is used to collect archive data and perform preprocessing and feature extraction, wherein the archive data includes text data, image data and audio data, and to perform multimodal fusion on the extracted archive features to obtain multimodal features, and map the multimodal features to a unified feature space through a weighted fusion method; wherein the archive features include text features, image features and audio features;

[0206] The feature extraction and fusion module 11 is used to construct an extraction method based on a joint multimodal convolutional network to extract multimodal features from archive features to generate high-order features, and to combine high-order features of different modalities using dynamic weighted fusion to generate a global multimodal feature representation;

[0207] The image restoration and enhancement module 12 is used to design a generative adversarial network based on multi-resolution residuals to restore blurred, damaged or missing parts in the archive image, and to perform adversarial training guided by multimodal features to output the restored image, and to design a post-processing module based on adaptive contrast enhancement to dynamically enhance the restored image;

[0208] The quantum computing acceleration retrieval module 13 is used to convert the final multimodal feature representation into a quantum state, and use the quantum amplitude amplification technology based on the Grover algorithm to amplify and search the quantum state. According to the results of the amplification and search, the best matching feature is extracted through quantum measurement, and the best matching feature is decoded into classical information.

[0209] The personalized intelligent retrieval and optimization module 14 is used to design a user behavior model based on dynamic adjustment of multimodal feature weights to extract user preferences, design a personalized retrieval model based on weighted interaction according to user preferences to optimize the sorting of retrieval results, and then design an adaptive optimization mechanism based on a feedback loop to optimize user preferences and personalized retrieval models by analyzing user feedback behaviors when using the system, and then design a recommendation algorithm based on user preferences and profile features to calculate the matching degree between user behavior features and profile data to provide personalized recommendation services;

[0210] The system adaptive learning and optimization module 15 is used to design a state space model to dynamically adjust the system state, use a Q-learning-based reinforcement learning algorithm to optimize the state space model and learn the optimal behavior strategy, and introduce a regularization term based on domain knowledge into the optimal behavior strategy to constrain the optimal behavior strategy in a specific field, dynamically adjust the multimodal features according to the user's real-time feedback and environmental changes, and design a dynamic feedback mechanism based on incremental learning to perform incremental updates on the system;

[0211] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0212] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a division of logical functions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0213] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0214] Although embodiments of the present invention have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A file retrieval method based on AI images and key information of talents, characterized in that: The method comprises: S1. Collecting archival data and performing preprocessing and feature extraction, wherein the archival data includes text data, image data and audio data, and performing multimodal fusion on the extracted archival features to obtain multimodal features, and mapping the multimodal features to a unified feature space through a weighted fusion method; wherein the archival features include text features, image features and audio features; S2. Construct an extraction method based on a joint multimodal convolutional network to extract multimodal features from archive features to generate high-order features, and use dynamic weighted fusion to combine high-order features of different modalities to generate a global multimodal feature representation; S3. Design a generative adversarial network based on multi-resolution residuals to repair blurred, damaged or missing parts in archival images, conduct adversarial training guided by multimodal features to output the repaired images, and design a post-processing module based on adaptive contrast enhancement to dynamically enhance the repaired images; S4. Convert the final multimodal feature representation into a quantum state, use quantum amplitude amplification technology based on Grover algorithm to amplify and search the quantum state, extract the best matching feature through quantum measurement according to the amplification and search results, and decode the best matching feature into classical information; S5. Design a user behavior model based on dynamic adjustment of multimodal feature weights to extract user preferences. Design a personalized retrieval model based on weighted interaction to optimize the ranking of retrieval results according to user preferences. Then design an adaptive optimization mechanism based on feedback loops to optimize user preferences and personalized retrieval models by analyzing user feedback when using the system. Then design a recommendation algorithm based on user preferences and profile features to calculate the matching degree between user behavior features and profile data for personalized recommendation services. S6. Design a state-space model to dynamically adjust the system state. Use a reinforcement learning algorithm based on Q-learning to optimize the state-space model and learn the optimal behavior strategy. At the same time, introduce a regularization term based on domain knowledge into the optimal behavior strategy to constrain the optimal behavior strategy in a specific domain. Dynamically adjust the multimodal features based on real-time user feedback and environmental changes, and design a dynamic feedback mechanism based on incremental learning to perform incremental updates on the system.

2. According to claim 1, a file retrieval method based on AI images and key information of talents is characterized in that: When extracting text features, we introduce domain-specific regularization terms based on the key terms in a specific field contained in the text data in the scene to strengthen the expression of specific terms; when extracting image features, we introduce processing terms based on gradient regularization to constrain the gradient changes of the image; when extracting audio features, we introduce regularization terms based on spectrum smoothing to constrain the high-frequency noise in the spectrum; The weighted fusion method is: Unify the features of each modality into the shared feature space M through linear mapping unified , the specific formula is as follows: Among them, W T , W I , W A are the linear mapping matrices of text, image, and audio features, respectively, T final ,I final , A final are the feature representations of text, image, and audio features respectively. is the regularization term used to balance the characteristics of different modes, λ fusion In order to integrate the weight of the regularization term, the fusion regularization term is introduced Form the final multimodal feature representation M final , which is expressed as follows: Among them, λ balance is the regularization coefficient.

3. The archive retrieval method based on AI images and key information of talents according to claim 1, characterized in that: The extraction method based on the joint multimodal convolutional network specifically comprises the following steps: For each modality of data, design a joint multimodal convolution kernel W m , acting on the feature representation of each modality to generate a high-order feature map F m , which is expressed as follows: F m =σ(W m ·T final )+σ(W m ·I final )+σ(W m ·A final ) Among them, σ(·) is a nonlinear activation function, W m is a multimodal convolution kernel; The dynamic weighted fusion is expressed as follows: Among them, α m is the weight of each modal feature, F m is the high-order feature map of the mth mode, M fusion Represents weighted fusion of multimodal features; At the same time, a mechanism based on entropy regularization is introduced in the weighted fusion of multimodal features. Constraining a certain modal feature to be overly dominant in the fusion process, the final multimodal feature representation M final It is expressed as follows: Among them, λ fusion is the regularization coefficient, To regularize the fused feature entropy.

4. The archive retrieval method based on AI images and key information of talents according to claim 1, characterized in that: The multi-resolution residual-based generative adversarial network includes a generator and a discriminator, both of which are optimized and image repaired through adversarial training; The design of the generator network is based on a multi-resolution residual module, and its core operation formula is expressed as follows: Among them, G(·) is the generator network, X damaged is the input damaged image, X repaired is the output repaired image, L is the number of layers of the multi-resolution residual module, and W l is the convolution weight matrix of the lth layer, ResBlock l (· is the residual module of the lth layer; The output of the discriminator network is expressed as follows: D(X,M final )=σ(W d ·[X,M final ]+b d ) Where D(·) is the discriminator network, X is the input image, and M final is the multimodal fusion feature obtained from the first two steps, W d is the weight matrix of the discriminator, b d is the bias term, σ(·) is the activation function; The loss function of the generator is expressed as follows: Among them, λ structure is the weight coefficient of the regularization term, is a structural regularization term used to constrain the local structure of the image.

5. The archive retrieval method based on AI images and key information of talents according to claim 3 is characterized in that: The final multimodal feature representation M final Mapped to the quantum state, let the final multimodal characteristics Map it to the quantum state ψ M , which is expressed as follows: Among them, M i is the feature vector M final The i-th component of final ∥ is the Euclidean norm of the eigenvector, e i is the quantum state basis vector; When encoding quantum states, a regularization term based on feature sparsity is introduced Optimizing the quantum state ψ M The expression is as follows:

6. The archive retrieval method based on AI images and key information of talents according to claim 5, characterized in that: The quantum amplitude amplification technology based on the Grover algorithm specifically includes: Denote the quantum state of the target feature as ψ target , the quantum state in the database is represented by ψ M , through the quantum operator of Grover's algorithm For the initial quantum state ψ M Amplify the amplitude so that the final quantum state ψ search and the target quantum state ψ target The overlap probability is maximized; wherein the Grover algorithm is expressed as follows: Among them, ψ target is the quantum state representation of the target feature, ψ M is the quantum state representation in the database, is the quantum operator of Grover's algorithm, <ψ M |ψ target > is the overlap between the target state and the database state; Introducing a weighting mechanism based on multimodal information entropy into the Grover algorithm The quantum state is weighted according to the information entropy of the multimodal features, so that the feature with the highest information content has the largest weight in the search, which is expressed as follows:

7. The archive retrieval method based on AI images and key information of talents according to claim 6 is characterized in that: The best matching feature is extracted by quantum measurement, and the quantum state ψ is calculated search The ground state e with the highest probability amplitude i Determine the best matching feature, where the quantum state ψ search The probability calculation formula for measurement is expressed as follows: P(i)=| <e i |ψ search >| 2 Where P(i) is the measurement result for the ground state e i The probability of <e i |ψ search > is the quantum state ψ search In the ground state i The projection amplitude on ; At the same time in the quantum state ψ search The probability calculation formula of the measurement introduces a regularization term based on the measurement error Reduce the impact of quantum noise, expressed as follows:

8. The archive retrieval method based on AI images and key information of talents according to claim 1, characterized in that: The user behavior model based on dynamic adjustment of multimodal feature weights is expressed as follows: Among them, U t is the user behavior feature, represents the user preference, n represents the total number of users, m represents the total number of multimodal features, B t,i is the i-th user behavior data at time point t, β i is the weight of the corresponding behavior data, is a regularization function based on time decay and behavior correlation, M final,j is the jth multimodal feature, γ j is the dynamic adjustment coefficient of multimodal features, λ multi is the fusion weight of multimodal features and user behavior features; The personalized retrieval model calculates the relevance score of each archive by matching the user behavior characteristics and the archive multimodal characteristics, and sorts the retrieval results according to the score, wherein the score is calculated as follows: Among them, S i is the personalized score of the i-th profile, W s is the sorting weight matrix, M i is the multimodal feature vector of the ith archive, U t is the user behavior feature vector, ⊙ is the weighted interaction operation of the feature, is the context regularization term, λ context is its weight; And introduce a regularization term based on content diversity in the score calculation formula To constrain the search content, it is expressed as follows: Among them, sim(M i ,M j ) is the feature similarity between files i and j, Relevance(S i ,S j ) is their relevance in the sorting.

9. The archive retrieval method based on AI images and key information of talents according to claim 1, characterized in that: The adaptive learning formula for state updating of the state space model is expressed as follows: Among them, W s , W a , W f are the weight matrices of system status, behavior strategy and user feedback, respectively, adapt is the weight of the adaptive regularization term, is an adaptive regularization term used to balance the relationship between the current state, behavior strategy, and user feedback.

10. A file retrieval system based on AI images and key information of talents, characterized in that: The system comprises: A data acquisition module, used to collect archive data and perform preprocessing and feature extraction, wherein the archive data includes text data, image data and audio data, and to perform multimodal fusion on the extracted archive features to obtain multimodal features, and to map the multimodal features into a unified feature space through a weighted fusion method; wherein the archive features include text features, image features and audio features; The feature extraction and fusion module is used to construct an extraction method based on a joint multimodal convolutional network to extract multimodal features from archive features to generate high-order features, and use dynamic weighted fusion to combine high-order features of different modalities to generate a global multimodal feature representation; The image restoration and enhancement module is used to design a generative adversarial network based on multi-resolution residuals to restore blurred, damaged or missing parts in archival images, and conduct adversarial training guided by multimodal features to output the restored images. A post-processing module based on adaptive contrast enhancement is designed to dynamically enhance the restored images. The quantum computing acceleration retrieval module is used to convert the final multimodal feature representation into a quantum state, and use the quantum amplitude amplification technology based on the Grover algorithm to amplify and search the quantum state. According to the amplification and search results, the best matching features are extracted through quantum measurement, and the best matching features are decoded into classical information; The personalized intelligent retrieval and optimization module is used to design a user behavior model based on dynamic adjustment of multimodal feature weights to extract user preferences, design a personalized retrieval model based on weighted interaction according to user preferences to optimize the ranking of retrieval results, and then design an adaptive optimization mechanism based on feedback loops to optimize user preferences and personalized retrieval models by analyzing user feedback behaviors when using the system. Then, a recommendation algorithm based on user preferences and profile features is designed to calculate the matching degree between user behavior features and profile data for personalized recommendation services. The system adaptive learning and optimization module is used to design a state-space model to dynamically adjust the system state. It uses a reinforcement learning algorithm based on Q-learning to optimize the state-space model and learn the optimal behavior strategy. At the same time, it introduces a regularization term based on domain knowledge into the optimal behavior strategy to constrain the optimal behavior strategy in a specific field. It dynamically adjusts the multimodal features according to the user's real-time feedback and environmental changes, and designs a dynamic feedback mechanism based on incremental learning to perform incremental updates on the system.

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