Multi-modal diagnosis and treatment data analysis and auxiliary diagnosis method for Internet medical treatment

Abstract

The invention discloses a multi-modal diagnosis and treatment data analysis and auxiliary diagnosis method for internet medical treatment, which comprises the following steps: S1, performing feature extraction and fusion on multi-modal diagnosis and treatment data through a nonlinear mapping algorithm based on quantum state optimization; s2, constructing a self-adaptive dynamic relation network, and capturing a complex interaction relation between cross-domain features in the diagnosis and treatment data; s3, fusing implicit features in the sparse medical data with semantic association; s4, optimizing the diagnosis and treatment decision process by applying a causal inference technology; s5, processing anomalies in the diagnosis and treatment data by using a fusion generative adversarial network and sparse tensor decomposition technology; s6, constructing a decentralized encrypted federated learning network to protect the privacy of the patient; s7, predicting a disease evolution path in real time to form dynamic decision support; and S8, performing accelerated analysis on the high-dimensional feature mode in the diagnosis and treatment data in combination with quantum calculation. The method has the advantages of high data fusion capability, high diagnosis precision and high real-time decision support capability.

Description

Technical Field

[0001] The present invention relates to the field of Internet medical technology, and in particular to a multimodal diagnosis and treatment data analysis and auxiliary diagnosis method for Internet medical treatment. Background Art

[0002] With the rapid development of information technology and medical services, Internet medical care has become an important means to improve the utilization rate of medical resources and diagnostic efficiency. However, the core data source of Internet medical care - multimodal diagnosis and treatment data, including data of different modalities such as text, images, and time series signals, still faces major challenges in its fusion and analysis, which directly affects the diagnosis and treatment efficiency and accuracy of intelligent medical systems. Multimodal diagnosis and treatment data contains rich disease information and clinical characteristics, and is an important basis for auxiliary diagnosis and decision-making. However, the diversity and complexity of data characteristics make it difficult for existing diagnosis and treatment data analysis methods to meet the needs in practical applications.

[0003] In the existing technology, traditional multimodal diagnosis and treatment data analysis methods mainly rely on single-modal analysis or shallow feature fusion models. These methods have many shortcomings when facing the complexity, sparsity and privacy protection requirements of multimodal data. Specifically, the existing technology has obvious defects in the following aspects:

[0004] 1. Insufficient multimodal data fusion capabilities: Existing methods often find it difficult to effectively capture the deep correlations between multimodal data, resulting in a lack of uniformity and completeness in the feature representation after data fusion, which limits the performance of the diagnostic model.

[0005] 2. Weak privacy protection mechanism: Traditional methods for privacy protection of medical data mainly rely on simple encryption or permission control, which is difficult to meet the high standards for data privacy in distributed data collaborative analysis, increasing the risk of patient privacy leakage.

[0006] 3. Insufficient ability to handle data sparsity and imbalance: Existing technologies have weak analytical capabilities for rare diseases and abnormal data. In scenarios with sparse or imbalanced data, the performance of diagnostic models decreases significantly.

[0007] 4. Lack of real-time and accuracy in diagnostic decisions: Traditional diagnostic models are mostly based on fixed rules or shallow learning, which makes it difficult to cope with the dynamic evolution of complex pathological patterns and lacks comprehensive capabilities to support real-time decision-making.

[0008] 5. Lack of unified spatiotemporal dynamic modeling capabilities: Existing technologies are insufficient in their ability to model the spatiotemporal characteristics of disease evolution and are unable to provide dynamic prediction and diagnostic support for disease trends, leading to inaccurate decision-making.

[0009] Therefore, how to provide a multimodal diagnosis and treatment data analysis and auxiliary diagnosis method for Internet medical care is an urgent problem that technicians in this field need to solve. Summary of the invention

[0010] One purpose of the present invention is to propose a multimodal diagnosis and treatment data analysis and auxiliary diagnosis method for Internet medical care. The present invention makes full use of advanced algorithms such as multimodal data fusion, privacy protection diagnosis model, causal inference technology, spatiotemporal dynamic modeling and quantum computing, and describes in detail the technical solution for intelligent analysis of multimodal diagnosis and treatment data and real-time auxiliary diagnosis. It has the advantages of strong data fusion capability, high privacy protection, high diagnostic accuracy and strong real-time decision support capability.

[0011] According to an embodiment of the present invention, a multimodal diagnosis and treatment data analysis and auxiliary diagnosis method for Internet medical care includes the following steps:

[0012] S1. Feature extraction and fusion of multimodal diagnosis and treatment data through nonlinear mapping algorithm based on quantum state optimization;

[0013] S2. Construct an adaptive dynamic relationship network and use time-sensitive modeling technology to capture the complex interactive relationships between cross-domain features in multimodal diagnosis and treatment data;

[0014] S3, a medical knowledge graph enhancement method based on dynamic semantic reasoning, integrating implicit features and semantic associations in sparse medical data;

[0015] S4. Apply causal inference technology to optimize the diagnosis and treatment decision-making process, and generate diagnosis and treatment pathways for complex pathological patterns through causal relationship identification and intervention simulation;

[0016] S5. Use the technology of integrating generative adversarial networks and sparse tensor decomposition to synthesize and complete the abnormal missing values ​​in the diagnosis and treatment data;

[0017] S6. Build a decentralized encrypted federated learning network to protect patient privacy, conduct multi-party data collaborative modeling, and enhance diagnostic efficiency and security;

[0018] S7. Based on the spatiotemporal dynamic relationship modeling technology, the disease evolution path is predicted in real time to form dynamic decision support;

[0019] S8. Combine quantum computing with complex pattern recognition algorithms to accelerate the analysis of high-dimensional feature patterns in diagnostic data.

[0020] Optionally, the S1 specifically includes:

[0021] S11. Preliminarily classify multimodal diagnosis and treatment data into structured data, unstructured data, and high-dimensional time series data, and use multi-resolution coding strategies to pre-process the data in a unified and standardized manner;

[0022] S12. Construct a high-dimensional quantum state mapping for the standardized data through a quantum state generation network, including embedding data features into the quantum state representation space and generating the initial quantum state. where w i represents the adaptive weight coefficient, |x i >Quantum state projection representing features;

[0023] S13. Based on the characteristic optimization model controlled by dynamic Hamiltonian, the optimization objective function is defined:

[0024]

[0025] Where S ij is the feature similarity matrix, c i and c j is the weight coefficient, μ is the sparsity adjustment parameter, and the feature weight distribution is optimized by the variational quantum algorithm;

[0026] S14. Using quantum superposition analysis technology, the optimized features are projected into the efficient feature subspace, and redundant and irrelevant features are eliminated by measuring the importance of the features:

[0027] H(|ψ>)=-∑ i p i log(p i );

[0028] Among them, p i represents the probability amplitude of the characteristic state;

[0029] S15. Use quantum entanglement characteristics to build a feature fusion network, generate joint feature representation of multimodal data through parameterized multi-body entanglement gate operations, and finally output the fusion feature vector |φ>=U(θ)|ψ>, where U(θ) is the optimized quantum gate matrix;

[0030] S16. Perform quantum state dimensionality reduction and standardized reorganization on the fused features to generate a unified multimodal diagnosis and treatment feature matrix, retaining the key information of high-dimensional features.

[0031] Optionally, the S2 specifically includes:

[0032] S21. Jointly model the time series data and non-time series data in the multimodal diagnosis and treatment data, use a heterogeneous graph structure to define the multimodal feature nodes and their cross-modal association relationships, and construct a heterogeneous feature interaction graph G = (V, E), where V represents the feature node set and E represents the interaction relationship between nodes;

[0033] S22. Update the interaction weights between cross-modal features through a dynamic graph neural network model. The dynamic weight update rule is:

[0034] Rij (t+1)=f(R ij (t),x i (t),x j (t),Δt);

[0035] Where R ij (t) represents the association weight of features i and j at time t, f is the dynamic update function, Δt is the time interval, x i (t) and x j (t) are the feature values ​​of nodes i and j at time t;

[0036] S23. Capture the diverse interactions between features based on the multi-head attention mechanism and construct a time-sensitive weight matrix:

[0037]

[0038] in and are the query and key vectors of the kth attention head at time t, d is the feature dimension, represents the time-related weight matrix;

[0039] S24. Combine the graph convolution layer and the time-dependent module to perform dynamic feature representation update. The node feature update rule is:

[0040]

[0041] in is the feature representation of node i at the kth layer, N(i) is the set of adjacent nodes of node i, and W )k is the learnable weight matrix, σ is the nonlinear activation function;

[0042] S25. Optimize the feature state transfer process through the adaptive time weight control mechanism, and calculate the node weight transfer parameters using the time sensitivity function:

[0043]

[0044] where α k is the weight coefficient of the k-th layer attention head, W t is the time-dependent feature weight matrix;

[0045] S26. Generate a final feature representation matrix that includes dynamic relationship interactions, deeply integrate time dependency and cross-modal interaction characteristics, and form a final feature representation with multi-dimensional correlation optimization.

[0046] Optionally, the S3 specifically includes:

[0047] S31. Construct a basic medical knowledge graph, extract medical entities and relationships from sparse medical data, generate an initial graph structure, and map each entity and relationship to a low-dimensional vector space to generate the corresponding graph embedding;

[0048] S32. Based on the dynamic semantic reasoning method of graph embedding, design the update rules of entities and relationships, and calculate the update value of node embedding:

[0049]

[0050] in, is the set of relationships between entities, V R is the relation embedding, V e′ is the neighbor node embedding, φ and ψ are the embedding update functions;

[0051] S33. Build a dynamic weight allocation mechanism to adjust the importance of entities and relationships in the knowledge graph according to time dependency, and calculate the relationship weight adjustment matrix:

[0052] W R (t) = γ·W R (t-1)+(1-γ)·f(T);

[0053] Among them, γ is the time smoothing parameter, f(T) is the time characteristic function, W R (t) is the weight value of the relationship at time t;

[0054] S34, adopting a cross-entity semantic association enhancement model, performing weighted analysis on entities and their adjacency relationships through a graph attention mechanism, generating an enhanced semantic weight matrix, and using a sparse representation of graph embedding to generate a feature matrix enhanced by the knowledge graph;

[0055] S35. The dynamically adjusted knowledge graph is embedded and integrated into the final semantic feature representation, combining cross-temporal and cross-modal associations.

[0056] Optionally, the S4 specifically includes:

[0057] S41. Construct a causal relationship graph model based on dynamic feature weights, define the variables in the diagnosis and treatment data as nodes, and the potential causal relationship between the variables as directed edges, and generate a causal relationship graph G = (V, E, W), where W is the edge weight set, indicating the strength of the causal relationship;

[0058] S42. Use a causal structure search algorithm based on reinforcement learning to dynamically optimize the topological structure of the causal relationship graph:

[0059]

[0060] Where cycle(G) is the penalty term for the acyclic constraint, β is the regularization parameter, and P(v j |v i ) represents node v i and v j The conditional probability of

[0061] S43. Using the variational inference model for causal effect calculation, the intervention effect value is calculated by fitting the intervention distribution P(Y|do(X=x)):

[0062] Δ Y =∫ Z (P(Y|X=x,Z)-P(Y|X=x',Z))P(Z)dZ;

[0063] where Δ Y represents the change in intervention effect of the target variable Y, and Z is a potential confounding variable;

[0064] S44. Generate diagnosis and treatment pathways using the optimized causal relationship diagram, dynamically adjust the diagnosis and treatment intervention strategy by identifying the key variables and key paths in the causal relationship diagram, so that the optimal variable adjustment sequence satisfies the causal dependency of the complex pathological pattern;

[0065] S45. Construct an intervention simulation model based on a generative adversarial network to simulate the disease progression trajectory under different diagnosis and treatment pathways, generate real-time feedback data on diagnosis and treatment interventions, and output optimized diagnosis and treatment pathway plans.

[0066] Optionally, the S5 specifically includes:

[0067] S51. Constructing high-dimensional tensor representation of sparse medical data Where I, J, and K represent the dimensions of the data, respectively. Sparse matrix decomposition is used to initialize outliers and missing values ​​to generate the initial estimation matrix T init ;

[0068] S52, sparse data completion model based on generative adversarial network, defines generator G and discriminator D, the generator generates the completed tensor by combining context features

[0069]

[0070] Where ∈ is the embedded diagnosis and treatment feature noise vector, which is combined with the data pattern for completion operation;

[0071] S53, optimize the discriminator D through the dynamic discrimination mechanism, the discriminator will generate a tensor With the real data tensor T real The structural differences are quantified as conditional discriminant distributions:

[0072]

[0073] S54, using sparse tensor regularization constraints, define the objective function of the generator, and jointly optimize the generator G by sparsity and content fidelity:

[0074]

[0075] where ∥·∥1 is the L1 norm, α is the sparse regularization parameter, and T obs Represents the observed tensor data;

[0076] S55. Combined with the multimodal generative adversarial mechanism, the completed sparse tensor is gradually optimized to generate the final tensor T opt , and evaluate the completion quality through a multi-layer discriminator.

[0077] Optionally, the S6 specifically includes:

[0078] S61. Construct a federated learning network based on decentralized topology, with the participating node set P = P1, P2, ..., P n}, each node holds local diagnosis and treatment data D i and a unique encryption key pair

[0079] S62, using a distributed model update protocol based on homomorphic encryption, each node updates the local model parameter W i Encrypt and generate an encrypted model Where E is a homomorphic encryption function, which is uploaded to the federated server through a distributed encryption channel;

[0080] S63. In the federated server, a multi-layer security aggregation algorithm is used to perform weighted aggregation on the encrypted models uploaded by each node without decryption, and the aggregation result is calculated:

[0081]

[0082] where ω i For node P i The weight coefficient is dynamically adjusted according to the amount of data and task contribution;

[0083] S64, through the privacy-enhanced key exchange protocol, the server sends the aggregated encryption result W agg Distributed to all nodes, each node uses a private key Decrypt the aggregate model and compute local model updates:

[0084]

[0085] Where η is the adaptive learning rate, For node P iThe local loss gradient of ;

[0086] S65. Through the differential privacy perturbation mechanism, perturbation noise is added to the uploaded and distributed model parameters, and the noise distribution function N(0,σ 2 ) Ensure that the data privacy of each node is not leaked during the federated learning process;

[0087] S66. Build a dynamic model consistency verification mechanism in the federated learning process to monitor the integrity of encryption parameters and the credibility of aggregation results in real time, and generate the final federated model W through multiple rounds of iterative optimization. final and stored in each participating node in a decentralized manner.

[0088] Optionally, the S7 specifically includes:

[0089] S71. Construct dynamic spatiotemporal feature representation and model the disease data as a high-dimensional tensor containing time series and spatial associations. Where T is the time dimension, N is the number of nodes, and D is the feature dimension. Sparse coding is used to initialize the time and space feature representation;

[0090] S72. Based on the dynamic spatiotemporal graph network, the spatiotemporal features are represented as a dynamic graph structure G t =(V t ,E t ), where V t is the node set at time t, E t is the dynamic edge set at time t, and the spatiotemporal weight function is W(t,v i ,v j ), indicating the strength of association between nodes, and updating the dynamic graph structure;

[0091] S73, use graph neural network to model spatiotemporal features and update node features F through dynamic convolution mechanism v (t):

[0092]

[0093] Where φ is the feature interaction function, N(v) represents the neighbor set of node v, and σ is the nonlinear activation function;

[0094] S74. Adopt the spatiotemporal interactive attention mechanism to optimize the interaction weights of temporal and spatial features, and express the joint importance of time step t and spatial node s through the spatiotemporal joint attention matrix A(t,s):

[0095]

[0096] Where Q t and K s are query and key representations of temporal and spatial features, respectively, and dts for the joint dimension;

[0097] S75. Generate the disease evolution path through the dynamic recursive prediction model and predict the disease status

[0098]

[0099] Where g is a recursive mapping function that combines spatiotemporal features to generate multi-step prediction results;

[0100] S76. Update the prediction model in real time and generate dynamic disease evolution prediction results through multiple rounds of iterative optimization, which are used for spatiotemporal decision-making and intervention path generation in the diagnosis and treatment process.

[0101] Optionally, the S8 specifically includes:

[0102] S81. Perform high-dimensional feature embedding representation on diagnosis and treatment data and construct tensors containing multimodal features. Where N is the number of data samples, D is the feature dimension, and the quantum state of the tensor is initialized to generate quantum features where c i is the weight coefficient, |x i >Basic state;

[0103] S82. The high-dimensional feature pattern is encoded through the quantum superposition mechanism, and the quantum gate operation U(x) is used to act on the quantum state |ψ> to generate the encoded features:

[0104] |ψ enc > = U(x)|ψ>;

[0105] S83. Realize complex pattern recognition based on quantum coherent computing and design quantum Hamiltonian optimizer H=H diag +H inter , where H diag represents the independent attributes between features, H inter Represents the interaction between features by minimizing the quantum energy E=<ψ enc |H|ψ enc > Extract the main feature patterns;

[0106] S84. Use quantum Fourier transform to reduce the dimension of the characteristic pattern and calculate the frequency domain of the high-dimensional feature:

[0107]

[0108] where F k is the frequency domain feature after dimension reduction, x j is the original eigenvalue;

[0109] S85. Use the characteristics of quantum entanglement to build a multimodal feature fusion model, realize the correlation recognition of cross-modal feature patterns through the quantum control gate C(U), and generate the final fusion feature state |φ>;

[0110] S86. Output the fused feature pattern, perform accelerated analysis through the classical and quantum joint optimization algorithm, and generate complex pattern feature results for diagnosis and treatment analysis.

[0111] The beneficial effects of the present invention are:

[0112] (1) The present invention combines a variety of methods such as collaborative analysis of multimodal data, adaptive dynamic relationship modeling, causal inference technology and quantum computing to achieve deep integration and efficient analysis of complex diagnosis and treatment data, enabling the system to accurately explore the deep correlation between multimodal data and significantly improve the performance of the diagnostic model, especially in complex pathological patterns and sparse data scenarios.

[0113] (2) The present invention introduces distributed federated learning and homomorphic encryption technology to construct a privacy-preserving diagnostic model, thereby achieving high security and efficiency in multi-party collaborative modeling, ensuring that patient data privacy is not leaked during the diagnosis and treatment process, meeting strict medical privacy protection requirements, and enhancing data sharing and diagnostic capabilities.

[0114] (3) The present invention combines medical knowledge graphs with dynamic semantic reasoning, spatiotemporal dynamic relationship modeling, and generative adversarial networks to dynamically predict the disease evolution path and optimize the diagnosis and treatment path, forming real-time diagnostic decision support, greatly improving diagnostic efficiency and decision accuracy, and is particularly suitable for rapidly changing disease management scenarios.

[0115] (4) The present invention accelerates the analysis of high-dimensional complex patterns in medical data through nonlinear feature analysis and pattern recognition algorithms optimized by quantum states, thereby reducing computing costs, improving the real-time performance of medical data processing, and providing efficient and intelligent auxiliary support for complex medical scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0116] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0117] Figure 1 This is an overall framework diagram of the multimodal diagnosis and treatment data analysis and auxiliary diagnosis method for Internet medicine proposed in the present invention. DETAILED DESCRIPTION

[0118] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0119] refer to Figure 1 , a multimodal diagnosis and treatment data analysis and auxiliary diagnosis method for Internet medical care, comprising the following steps:

[0120] S1. Feature extraction and fusion of multimodal diagnosis and treatment data through nonlinear mapping algorithm based on quantum state optimization;

[0121] In this implementation, S1 specifically includes:

[0122] S11. Preliminarily classify multimodal diagnosis and treatment data into structured data, unstructured data, and high-dimensional time series data, and use multi-resolution coding strategies to pre-process the data in a unified and standardized manner;

[0123] S12. Construct a high-dimensional quantum state mapping for the standardized data through a quantum state generation network, including embedding data features into the quantum state representation space and generating the initial quantum state. where w i represents the adaptive weight coefficient, |x i >Quantum state projection representing features;

[0124] S13. Based on the characteristic optimization model controlled by dynamic Hamiltonian, the optimization objective function is defined:

[0125]

[0126] Where S ij is the feature similarity matrix, c i and c j is the weight coefficient, μ is the sparsity adjustment parameter, and the feature weight distribution is optimized by the variational quantum algorithm;

[0127] S14. Using quantum superposition analysis technology, the optimized features are projected into the efficient feature subspace, and redundant and irrelevant features are eliminated by measuring the importance of the features:

[0128] H(|ψ>)=-∑ i p i log(p i );

[0129] Among them, p i represents the probability amplitude of the characteristic state;

[0130] S15. Use quantum entanglement characteristics to build a feature fusion network, generate joint feature representation of multimodal data through parameterized multi-body entanglement gate operations, and finally output the fusion feature vector |φ>=U(θ)|ψ>, where U(θ) is the optimized quantum gate matrix;

[0131] S16. Perform quantum state dimensionality reduction and standardized reorganization on the fused features to generate a unified multimodal diagnosis and treatment feature matrix, retaining the key information of high-dimensional features.

[0132] S2. Construct an adaptive dynamic relationship network and use time-sensitive modeling technology to capture the complex interactive relationships between cross-domain features in multimodal diagnosis and treatment data;

[0133] In this implementation, S2 specifically includes:

[0134] S21. Jointly model the time series data and non-time series data in the multimodal diagnosis and treatment data, use a heterogeneous graph structure to define the multimodal feature nodes and their cross-modal association relationships, and construct a heterogeneous feature interaction graph G = (V, E), where V represents the feature node set and E represents the interaction relationship between nodes;

[0135] S22. Update the interaction weights between cross-modal features through a dynamic graph neural network model. The dynamic weight update rule is:

[0136] R ij (t+1)=f(R ij (t),x i (t),x j (t),Δt);

[0137] Where R ij (t) represents the association weight of features i and j at time t, f is the dynamic update function, Δt is the time interval, x i (t) and x j (t) are the feature values ​​of nodes i and j at time t;

[0138] S23. Capture the diverse interactions between features based on the multi-head attention mechanism and construct a time-sensitive weight matrix:

[0139]

[0140] in and are the query and key vectors of the kth attention head at time t, d is the feature dimension, represents the time-related weight matrix;

[0141] S24. Combine the graph convolution layer and the time-dependent module to perform dynamic feature representation update. The node feature update rule is:

[0142]

[0143] in is the feature representation of node i at the kth layer, N(i) is the set of adjacent nodes of node i, and W (k is the learnable weight matrix, σ is the nonlinear activation function;

[0144] S25. Optimize the feature state transfer process through the adaptive time weight control mechanism, and calculate the node weight transfer parameters using the time sensitivity function:

[0145]

[0146] where α k is the weight coefficient of the k-th layer attention head, W t is the time-dependent feature weight matrix;

[0147] S26. Generate a final feature representation matrix that includes dynamic relationship interactions, deeply integrate time dependency and cross-modal interaction characteristics, and form a final feature representation with multi-dimensional correlation optimization.

[0148] S3, a medical knowledge graph enhancement method based on dynamic semantic reasoning, integrating implicit features and semantic associations in sparse medical data;

[0149] In this implementation, S3 specifically includes:

[0150] S31. Construct a basic medical knowledge graph, extract medical entities and relationships from sparse medical data, generate an initial graph structure, and map each entity and relationship to a low-dimensional vector space to generate the corresponding graph embedding;

[0151] S32. Based on the dynamic semantic reasoning method of graph embedding, design the update rules of entities and relationships, and calculate the update value of node embedding:

[0152]

[0153] in, is the set of relationships between entities, V R is the relation embedding, V e′ is the neighbor node embedding, φ and ψ are the embedding update functions;

[0154] S33. Build a dynamic weight allocation mechanism to adjust the importance of entities and relationships in the knowledge graph according to time dependency, and calculate the relationship weight adjustment matrix:

[0155] W R (t) = γ·W R (t-1)+(1-γ)·f(T);

[0156] Among them, γ is the time smoothing parameter, f(T) is the time characteristic function, W R (t) is the weight value of the relationship at time t;

[0157] S34, adopting a cross-entity semantic association enhancement model, performing weighted analysis on entities and their adjacency relationships through a graph attention mechanism, generating an enhanced semantic weight matrix, and using a sparse representation of graph embedding to generate a feature matrix enhanced by the knowledge graph;

[0158] S35. The dynamically adjusted knowledge graph is embedded and integrated into the final semantic feature representation, combining cross-temporal and cross-modal associations.

[0159] S4. Apply causal inference technology to optimize the diagnosis and treatment decision-making process, and generate diagnosis and treatment pathways for complex pathological patterns through causal relationship identification and intervention simulation;

[0160] In this implementation, S4 specifically includes:

[0161] S41. Construct a causal relationship graph model based on dynamic feature weights, define the variables in the diagnosis and treatment data as nodes, and the potential causal relationship between the variables as directed edges, and generate a causal relationship graph G = (V, E, W), where W is the edge weight set, indicating the strength of the causal relationship;

[0162] S42. Use a causal structure search algorithm based on reinforcement learning to dynamically optimize the topological structure of the causal relationship graph:

[0163]

[0164] Where cycle(G) is the penalty term for the acyclic constraint, β is the regularization parameter, and P(v j |v i ) represents node v i and v j The conditional probability of

[0165] S43. Using the variational inference model for causal effect calculation, the intervention effect value is calculated by fitting the intervention distribution P(Y|do(X=x)):

[0166] Δ Y =∫ Z (P(Y|X=x,Z)-P(Y|X=x′,Z))P(Z)dZ;

[0167] where Δ Y represents the change in intervention effect of the target variable Y, and Z is a potential confounding variable;

[0168] S44. Generate diagnosis and treatment pathways using the optimized causal relationship diagram, dynamically adjust the diagnosis and treatment intervention strategy by identifying the key variables and key paths in the causal relationship diagram, so that the optimal variable adjustment sequence satisfies the causal dependency of the complex pathological pattern;

[0169] S45. Construct an intervention simulation model based on a generative adversarial network to simulate the disease progression trajectory under different diagnosis and treatment pathways, generate real-time feedback data on diagnosis and treatment interventions, and output optimized diagnosis and treatment pathway plans.

[0170] S5. Use the technology of integrating generative adversarial networks and sparse tensor decomposition to synthesize and complete the abnormal missing values ​​in the diagnosis and treatment data;

[0171] In this implementation, S5 specifically includes:

[0172] S51. Constructing high-dimensional tensor representation of sparse medical data Where I, J, and K represent the dimensions of the data, respectively. Sparse matrix decomposition is used to initialize outliers and missing values ​​to generate the initial estimation matrix T init ;

[0173] S52, sparse data completion model based on generative adversarial network, defines generator G and discriminator D, the generator generates the completed tensor by combining context features

[0174]

[0175] Where ∈ is the embedded diagnosis and treatment feature noise vector, which is combined with the data pattern for completion operation;

[0176] S53, optimize the discriminator D through the dynamic discrimination mechanism, the discriminator will generate a tensor With the real data tensor T real The structural differences are quantified as conditional discriminant distributions:

[0177]

[0178] S54, using sparse tensor regularization constraints, define the objective function of the generator, and jointly optimize the generator G by sparsity and content fidelity:

[0179]

[0180] where ∥·∥1 is the L1 norm, α is the sparse regularization parameter, and T obs Represents the observed tensor data;

[0181] S55. Combined with the multimodal generative adversarial mechanism, the completed sparse tensor is gradually optimized to generate the final tensor T opt, and evaluate the completion quality through a multi-layer discriminator.

[0182] S6. Build a decentralized encrypted federated learning network to protect patient privacy, conduct multi-party data collaborative modeling, and enhance diagnostic efficiency and security;

[0183] In this implementation, S6 specifically includes:

[0184] S61. Construct a federated learning network based on decentralized topology, with the participating node set P = P1, P2, ..., P n}, each node holds local diagnosis and treatment data D i and a unique encryption key pair

[0185] S62, using a distributed model update protocol based on homomorphic encryption, each node updates the local model parameter W i Encrypt and generate an encrypted model Where E is a homomorphic encryption function, which is uploaded to the federated server through a distributed encryption channel;

[0186] S63. In the federated server, a multi-layer security aggregation algorithm is used to perform weighted aggregation on the encrypted models uploaded by each node without decryption, and the aggregation result is calculated:

[0187]

[0188] where ω i For node P i The weight coefficient is dynamically adjusted according to the amount of data and task contribution;

[0189] S64, through the privacy-enhanced key exchange protocol, the server sends the aggregated encryption result W agg Distributed to all nodes, each node uses a private key Decrypt the aggregate model and compute local model updates:

[0190]

[0191] Where η is the adaptive learning rate, For node P i The local loss gradient of ;

[0192] S65. Through the differential privacy perturbation mechanism, perturbation noise is added to the uploaded and distributed model parameters, and the noise distribution function N(0,σ 2 ) Ensure that the data privacy of each node is not leaked during the federated learning process;

[0193] S66. Build a dynamic model consistency verification mechanism in the federated learning process to monitor the integrity of encryption parameters and the credibility of aggregation results in real time, and generate the final federated model W through multiple rounds of iterative optimization. final and stored in each participating node in a decentralized manner.

[0194] S7. Based on the spatiotemporal dynamic relationship modeling technology, the disease evolution path is predicted in real time to form dynamic decision support;

[0195] In this implementation, S7 specifically includes:

[0196] S71. Construct dynamic spatiotemporal feature representation and model the disease data as a high-dimensional tensor containing time series and spatial associations. Where T is the time dimension, N is the number of nodes, and D is the feature dimension. Sparse coding is used to initialize the time and space feature representation;

[0197] S72. Based on the dynamic spatiotemporal graph network, the spatiotemporal features are represented as a dynamic graph structure G t =(V t ,E t ), where V t is the node set at time t, E t is the dynamic edge set at time t, and the spatiotemporal weight function is W(t,v i ,v j ), indicating the strength of association between nodes, and updating the dynamic graph structure;

[0198] S73, use graph neural network to model spatiotemporal features and update node features F through dynamic convolution mechanism v (t):

[0199]

[0200] Where φ is the feature interaction function, N(v) represents the neighbor set of node v, and σ is the nonlinear activation function;

[0201] S74. Adopt the spatiotemporal interactive attention mechanism to optimize the interaction weights of temporal and spatial features, and express the joint importance of time step t and spatial node s through the spatiotemporal joint attention matrix A(t,s):

[0202]

[0203] Where Q t and K s are query and key representations of temporal and spatial features, respectively, and d ts for the joint dimension;

[0204] S75. Generate the disease evolution path through the dynamic recursive prediction model and predict the disease status

[0205]

[0206] Where g is a recursive mapping function that combines spatiotemporal features to generate multi-step prediction results;

[0207] S76. Update the prediction model in real time and generate dynamic disease evolution prediction results through multiple rounds of iterative optimization, which are used for spatiotemporal decision-making and intervention path generation in the diagnosis and treatment process.

[0208] S8. Combine quantum computing with complex pattern recognition algorithms to accelerate the analysis of high-dimensional feature patterns in diagnostic data.

[0209] In this implementation, S8 specifically includes:

[0210] S81. Perform high-dimensional feature embedding representation on diagnosis and treatment data and construct tensors containing multimodal features. Where N is the number of data samples, D is the feature dimension, and the quantum state of the tensor is initialized to generate quantum features where c i is the weight coefficient, |x i >Basic state;

[0211] S82. The high-dimensional feature pattern is encoded through the quantum superposition mechanism, and the quantum gate operation U(x) is used to act on the quantum state |ψ> to generate the encoded features:

[0212] |ψ enc > = U(x)|ψ>;

[0213] S83. Realize complex pattern recognition based on quantum coherent computing and design quantum Hamiltonian optimizer H=H diag +H inter , where H diag represents the independent attributes between features, H inter Represents the interaction between features by minimizing the quantum energy E=<ψ enc |H|ψ enc > Extract the main feature patterns;

[0214] S84. Use quantum Fourier transform to reduce the dimension of the characteristic pattern and calculate the frequency domain of the high-dimensional feature:

[0215]

[0216] where F k is the frequency domain feature after dimension reduction, x j is the original eigenvalue;

[0217] S85. Use the characteristics of quantum entanglement to build a multimodal feature fusion model, realize the correlation recognition of cross-modal feature patterns through the quantum control gate C(U), and generate the final fusion feature state |φ>;

[0218] S86. Output the fused feature pattern, perform accelerated analysis through the classical and quantum joint optimization algorithm, and generate complex pattern feature results for diagnosis and treatment analysis.

[0219] Embodiment 1:

[0220] In order to verify the feasibility of the present invention, the present invention is applied to the remote diagnosis and treatment platform of a large Internet medical center, which covers multimodal diagnosis and treatment data processing, including text medical records, imaging data and physiological monitoring signals. Since the platform needs to process a large amount of heterogeneous data at the same time and protect patient privacy, the requirements for data processing efficiency, diagnostic accuracy and privacy protection are extremely high. Recently, the platform has found problems of low data processing efficiency and high diagnostic errors in multiple cross-departmental collaborative diagnosis and treatment scenarios, especially the poor performance of symptom analysis for sparse and unbalanced data. In order to solve the above problems, the platform decided to deploy the multimodal diagnosis and treatment data analysis and auxiliary diagnosis system proposed in the present invention.

[0221] First, the platform uses the method of the present invention to uniformly preprocess multimodal diagnosis and treatment data. By combining the nonlinear mapping algorithm of quantum state optimization, the platform generates feature representations for imaging data, medical record text, and physiological signals respectively, and constructs a high-dimensional joint representation of multimodal data. In actual operation, the diagnosis and treatment data of patients with a rare disease involve multiple modalities and the sample size is extremely small, so it is difficult for previous systems to effectively analyze. The present invention enhances the importance of sparse samples through a dynamic feature weight allocation mechanism, and completes missing data through a generative adversarial network, thereby generating a complete feature matrix.

[0222] In the collaborative modeling stage of diagnosis and treatment data, the platform adopted a decentralized encrypted federated learning model. Each medical department uses local data for model training and uploads model parameters to the federated server through homomorphic encryption technology. Through multiple rounds of encrypted aggregation, the platform successfully generated a privacy-protected global diagnostic model. In a rare disease analysis task, the model was able to quickly identify key diagnosis and treatment features and achieve collaborative optimization with models from other departments, significantly improving diagnostic accuracy.

[0223] In addition, in response to the need for real-time prediction of the disease's evolution, the platform uses the spatiotemporal dynamic relationship modeling technology in the present invention. By constructing a dynamic spatiotemporal graph network, the platform models and analyzes the real-time monitoring data of an intensive care patient to predict the patient's disease evolution path. During the application process, the system uses a spatiotemporal interactive attention mechanism to perform real-time dynamic optimization of multimodal data, successfully predicting the patient's possible risk of cardiac arrest within the next 24 hours, and winning valuable intervention time for the medical team.

[0224] In order to verify the effect of the present invention in practical applications, the platform compared and analyzed the performance of diagnosis and treatment data processing efficiency, diagnosis accuracy and privacy protection before and after system deployment. The specific data are shown in Table 1 below:

[0225] Table 1 Comparison of data processing and diagnosis performance of Internet medical platforms

[0226]

[0227] As can be seen from Table 1, after the deployment of the present invention, the overall performance of the platform has been significantly improved. For example, the data processing efficiency has been reduced from an average of 32 minutes to 9 minutes, the diagnostic accuracy of sparse diseases has been improved from 68.5% to 92.4%, and the accuracy of disease prediction has been improved from 76.2% to 95.1%. In addition, the privacy protection mechanism has successfully reduced the risk of data leakage from 5.7% before deployment to only 0.2%.

[0228] In a specific case, when diagnosing a patient with a rare genetic disease, the present invention supplemented the patient's key gene sequence data through a generative adversarial network, optimized the diagnosis and treatment path by combining causal inference technology, and finally recommended the best treatment plan. The diagnostic accuracy was improved by nearly 30 percentage points compared with traditional methods, and the treatment cycle was shortened by 15 days.

[0229] The present invention significantly improves the intelligence level of diagnosis and treatment data processing and auxiliary diagnosis through the comprehensive integration of multimodal data collaborative analysis, adaptive dynamic modeling and privacy protection, and provides strong technical support for efficient diagnosis and treatment and precise intervention of complex diseases.

[0230] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A multimodal diagnosis and treatment data analysis and auxiliary diagnosis method for Internet medical care, characterized in that: The steps include: S1. Feature extraction and fusion of multimodal diagnosis and treatment data through nonlinear mapping algorithm based on quantum state optimization; S2. Construct an adaptive dynamic relationship network and use time-sensitive modeling technology to capture the complex interactive relationships between cross-domain features in multimodal diagnosis and treatment data; S3, a medical knowledge graph enhancement method based on dynamic semantic reasoning, integrating implicit features and semantic associations in sparse medical data; S4. Apply causal inference technology to optimize the diagnosis and treatment decision-making process, and generate diagnosis and treatment pathways for complex pathological patterns through causal relationship identification and intervention simulation; S5. Use the technology of integrating generative adversarial networks and sparse tensor decomposition to synthesize and complete the abnormal missing values ​​in the diagnosis and treatment data; S6. Build a decentralized encrypted federated learning network to protect patient privacy, conduct multi-party data collaborative modeling, and enhance diagnostic efficiency and security; S7. Based on the spatiotemporal dynamic relationship modeling technology, the disease evolution path is predicted in real time to form dynamic decision support; S8. Combine quantum computing with complex pattern recognition algorithms to accelerate the analysis of high-dimensional feature patterns in diagnostic data.

2. According to claim 1, a multimodal diagnosis and treatment data analysis and auxiliary diagnosis method for Internet medical care is characterized in that: The S1 specifically includes: S11. Preliminarily classify multimodal diagnosis and treatment data into structured data, unstructured data, and high-dimensional time series data, and use multi-resolution coding strategies to pre-process the data in a unified and standardized manner; S12. Construct a high-dimensional quantum state mapping for the standardized data through a quantum state generation network, including embedding data features into the quantum state representation space and generating the initial quantum state. where w i represents the adaptive weight coefficient, |x i >Quantum state projection representing features; S13. Based on the characteristic optimization model controlled by dynamic Hamiltonian, the optimization objective function is defined: Where S ij is the feature similarity matrix, c i and c j is the weight coefficient, μ is the sparsity adjustment parameter, and the feature weight distribution is optimized by the variational quantum algorithm; S14. Using quantum superposition analysis technology, the optimized features are projected into the efficient feature subspace, and redundant and irrelevant features are eliminated by measuring the importance of the features: H(|ψ>)=-∑ i p i log(p i ); Among them, p i represents the probability amplitude of the characteristic state; S15. Use quantum entanglement characteristics to build a feature fusion network, generate joint feature representation of multimodal data through parameterized multi-body entanglement gate operations, and finally output the fusion feature vector |φ>=U(θ)|ψ>, where U(θ) is the optimized quantum gate matrix; S16. Perform quantum state dimensionality reduction and standardized reorganization on the fused features to generate a unified multimodal diagnosis and treatment feature matrix, retaining the key information of high-dimensional features.

3. The multimodal diagnosis and treatment data analysis and auxiliary diagnosis method of Internet medical care according to claim 1, characterized in that: The S2 specifically includes: S21. Jointly model the time series data and non-time series data in the multimodal diagnosis and treatment data, use a heterogeneous graph structure to define the multimodal feature nodes and their cross-modal association relationships, and construct a heterogeneous feature interaction graph G = (V, E), where V represents the feature node set and E represents the interaction relationship between nodes; S22. Update the interaction weights between cross-modal features through a dynamic graph neural network model. The dynamic weight update rule is: R ij (t+1)=f(R ij (t),x i (t),x j (t),Δt); Where R ij (t) represents the association weight of features i and j at time t, f is the dynamic update function, Δt is the time interval, x i (t) and x j (t) are the feature values ​​of nodes i and j at time t; S23. Capture the diverse interactions between features based on the multi-head attention mechanism and construct a time-sensitive weight matrix: in and are the query and key vectors of the kth attention head at time t, respectively, d is the feature dimension, represents the time-related weight matrix; S24. Combine the graph convolution layer and the time-dependent module to perform dynamic feature representation update. The node feature update rule is: in is the feature representation of node i at the kth layer, N(i) is the set of adjacent nodes of node i, and W (k) is the learnable weight matrix, σ is the nonlinear activation function; S25. Optimize the feature state transfer process through the adaptive time weight control mechanism, and calculate the node weight transfer parameters using the time sensitivity function: where α k is the weight coefficient of the k-th layer attention head, W t is the time-dependent feature weight matrix; S26. Generate a final feature representation matrix that includes dynamic relationship interactions, deeply integrate time dependency and cross-modal interaction characteristics, and form a final feature representation with multi-dimensional correlation optimization.

4. The multimodal diagnosis and treatment data analysis and auxiliary diagnosis method of Internet medical care according to claim 1, characterized in that: The S3 specifically includes: S31. Construct a basic medical knowledge graph, extract medical entities and relationships from sparse medical data, generate an initial graph structure, and map each entity and relationship to a low-dimensional vector space to generate the corresponding graph embedding; S32. Based on the dynamic semantic reasoning method of graph embedding, design the update rules of entities and relationships, and calculate the update value of node embedding: in, is the set of relationships between entities, V R is the relation embedding, V e′ is the neighbor node embedding, φ and ψ are the embedding update functions; S33. Build a dynamic weight allocation mechanism to adjust the importance of entities and relationships in the knowledge graph according to time dependency, and calculate the relationship weight adjustment matrix: W R (t)=γ·W R (t-1)+(1-γ)·f(T); Among them, γ is the time smoothing parameter, f(T) is the time characteristic function, W R (t) is the weight value of the relationship at time t; S34, adopting a cross-entity semantic association enhancement model, performing weighted analysis on entities and their adjacency relationships through a graph attention mechanism, generating an enhanced semantic weight matrix, and using a sparse representation of graph embedding to generate a feature matrix enhanced by the knowledge graph; S35. The dynamically adjusted knowledge graph is embedded and integrated into the final semantic feature representation, combining cross-temporal and cross-modal associations.

5. The multimodal diagnosis and treatment data analysis and auxiliary diagnosis method of Internet medical care according to claim 1, characterized in that: The S4 specifically includes: S41. Construct a causal graph model based on dynamic feature weights, define the variables in the diagnosis and treatment data as nodes, and the potential causal relationships between the variables as directed edges, and generate a causal graph G = (V, E, W), where W is the edge weight set, indicating the strength of the causal relationship; S42. Use a causal structure search algorithm based on reinforcement learning to dynamically optimize the topological structure of the causal relationship graph: Where cycle(G) is the penalty term for the acyclic constraint, β is the regularization parameter, and P(v j |v i ) represents node v i and v j The conditional probability of S43. Using the variational inference model for causal effect calculation, the intervention effect value is calculated by fitting the intervention distribution P(Y|do(X=x)): Δ Y =∫ Z (P(Y|X=x,Z)-P(Y|X=x',Z))P(Z)dZ\ where Δ Y represents the change in intervention effect of the target variable Y, and Z is a potential confounding variable; S44. Generate diagnosis and treatment pathways using the optimized causal relationship diagram, dynamically adjust the diagnosis and treatment intervention strategy by identifying the key variables and key paths in the causal relationship diagram, so that the optimal variable adjustment sequence satisfies the causal dependency of the complex pathological pattern; S45. Construct an intervention simulation model based on a generative adversarial network to simulate the disease progression trajectory under different diagnosis and treatment pathways, generate real-time feedback data on diagnosis and treatment interventions, and output optimized diagnosis and treatment pathway plans.

6. The multimodal diagnosis and treatment data analysis and auxiliary diagnosis method of Internet medical care according to claim 1, characterized in that: The S5 specifically includes: S51. Constructing high-dimensional tensor representation of sparse medical data Where I, J, and K represent the dimensions of the data, respectively. Sparse matrix decomposition is used to initialize outliers and missing values ​​to generate the initial estimation matrix T init ; S52, sparse data completion model based on generative adversarial network, defines generator G and discriminator D, the generator generates the completed tensor by combining context features Where ∈ is the embedded diagnosis and treatment feature noise vector, which is combined with the data pattern for completion operation; S53, optimize the discriminator D through the dynamic discrimination mechanism, the discriminator will generate a tensor With the real data tensor T real The structural differences are quantified as conditional discriminant distributions: S54. Use sparse tensor regularization constraints to define the objective function of the generator, and jointly optimize the generator G by sparsity and content fidelity: where ∥·∥1 is the L1 norm, α is the sparse regularization parameter, and T obs Represents the observed tensor data; S55. Combined with the multimodal generative adversarial mechanism, the completed sparse tensor is gradually optimized to generate the final tensor T opt , and evaluate the completion quality through a multi-layer discriminator.

7. The multimodal diagnosis and treatment data analysis and auxiliary diagnosis method of Internet medical care according to claim 1, characterized in that: The S6 specifically includes: S61. Construct a federated learning network based on decentralized topology, where the participating node set is P = {P1, P2, ..., P n }, each node holds local diagnosis and treatment data D i and a unique encryption key pair S62, using a distributed model update protocol based on homomorphic encryption, each node updates the local model parameter W i Encrypt and generate an encrypted model Where E is a homomorphic encryption function, which is uploaded to the federated server through a distributed encryption channel; S63. In the federated server, a multi-layer security aggregation algorithm is used to perform weighted aggregation on the encrypted models uploaded by each node without decryption, and the aggregation result is calculated: where ω i For node P i The weight coefficient is dynamically adjusted according to the amount of data and task contribution; S64, through the privacy-enhanced key exchange protocol, the server sends the aggregated encryption result W agg Distributed to all nodes, each node uses a private key Decrypt the aggregate model and compute local model updates: Where η is the adaptive learning rate, For node P i The local loss gradient of ; S65. Through the differential privacy perturbation mechanism, perturbation noise is added to the uploaded and distributed model parameters, and the noise distribution function N(0,σ 2 ) Ensure that the data privacy of each node is not leaked during the federated learning process; S66. Build a dynamic model consistency verification mechanism in the federated learning process to monitor the integrity of encryption parameters and the credibility of aggregation results in real time, and generate the final federated model W through multiple rounds of iterative optimization. final and stored in each participating node in a decentralized manner.

8. The multimodal diagnosis and treatment data analysis and auxiliary diagnosis method of Internet medical care according to claim 1, characterized in that: The S7 specifically includes: S71. Construct dynamic spatiotemporal feature representation and model disease data as a high-dimensional tensor containing time series and spatial associations. Where T is the time dimension, N is the number of nodes, and D is the feature dimension. Sparse coding is used to initialize the time and space feature representation; S72. Based on the dynamic spatiotemporal graph network, the spatiotemporal features are represented as a dynamic graph structure G t =(V t ,E t ), where V t is the node set at time t, E t is the dynamic edge set at time t, and the spatiotemporal weight function is W(t,v i ,v j ), indicating the strength of association between nodes, and updating the dynamic graph structure; S73, use graph neural network to model spatiotemporal features, and update node features F through dynamic convolution mechanism v (t): Where φ is the feature interaction function, N(v) represents the neighbor set of node v, and σ is the nonlinear activation function; S74. Adopt the spatiotemporal interactive attention mechanism to optimize the interaction weights of temporal and spatial features, and express the joint importance of time step t and spatial node s through the spatiotemporal joint attention matrix A(t,s): Where Q t and K s are query and key representations of temporal and spatial features, respectively, and d ts for the joint dimension; S75. Generate the disease evolution path through the dynamic recursive prediction model and predict the disease status Where g is a recursive mapping function that combines spatiotemporal features to generate multi-step prediction results; S76. Update the prediction model in real time and generate dynamic disease evolution prediction results through multiple rounds of iterative optimization, which are used for spatiotemporal decision-making and intervention path generation in the diagnosis and treatment process.

9. The multimodal diagnosis and treatment data analysis and auxiliary diagnosis method of Internet medical care according to claim 1, characterized in that: The S8 specifically includes: S81. Perform high-dimensional feature embedding representation on diagnosis and treatment data and construct tensors containing multimodal features. Where N is the number of data samples, D is the feature dimension, and the quantum state of the tensor is initialized to generate quantum features where c i is the weight coefficient, |x i >Basic state; S82. The high-dimensional feature pattern is encoded through the quantum superposition mechanism, and the quantum gate operation U(x) is used to act on the quantum state |ψ> to generate the encoded features: |ψ enc >=U(x)|ψ>; S83. Realize complex pattern recognition based on quantum coherent computing and design quantum Hamiltonian optimizer H=H diag +H inter , where H diag represents the independent attributes between features, H inter Represents the interaction between features by minimizing the quantum energy E = ψ enc |H|ψ enc > Extract the main feature patterns; S84. Use quantum Fourier transform to reduce the dimension of the characteristic pattern and calculate the frequency domain of the high-dimensional feature: where F k is the frequency domain feature after dimension reduction, x j is the original eigenvalue; S85. Use the characteristics of quantum entanglement to build a multimodal feature fusion model, realize the correlation recognition of cross-modal feature patterns through the quantum control gate C(U), and generate the final fusion feature state |φ>; S86. Output the fused feature pattern, perform accelerated analysis through the classical and quantum joint optimization algorithm, and generate complex pattern feature results for diagnosis and treatment analysis.

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