Dynamic updating method for expert portrait description

Abstract

The invention discloses a dynamic updating method for expert portrait description, which comprises the following steps: S1, constructing a multi-source heterogeneous data acquisition module, and acquiring data of experts in real time through distributed acquisition nodes; s2, performing embedded representation on the collected heterogeneous data, and realizing dynamic semantic feature extraction in combination with a context sensing model; s3, distributing weight parameters according to real-time dynamic characteristics, and accurately optimizing feature distribution of the portrait; s4, iteratively optimizing the model parameters through an incremental training algorithm, and correcting the feature change of the expert portrait in real time; s5, generating a future trend model of the expert portrait in combination with the historical feature change rate and the current data; s6, real-time storage and efficient access of portrait data are realized through a distributed storage architecture; and S7, according to the dynamically updated portrait, generating a targeted recommendation scheme and feeding back the targeted recommendation scheme to a user decision system in real time. The method has the advantages of high timeliness, high precision and high system adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of expert portraits, and in particular to a dynamic updating method for expert portrait characterization. Background Art

[0002] With the rapid development of artificial intelligence and big data technology, expert profiling has become an important research direction in many fields. By constructing expert portraits, it can play a key role in knowledge management, intelligent recommendation, resource allocation, etc. However, the existing expert profiling technology still has many shortcomings in terms of dynamics and accuracy, and it is difficult to meet the rapidly changing industry needs and diversified application scenarios.

[0003] In the existing technology, traditional expert profiling methods are usually based on static data and fixed models. These methods have obvious limitations when dealing with dynamic changes in expert characteristics and real-time decision-making needs. Specifically, traditional methods have obvious defects in the following aspects:

[0004] 1. Lack of dynamic updating capabilities: Existing expert portraits are mostly based on fixed-period data updates and model training, which cannot capture changes in expert characteristics in real time, causing the portrait results to lag behind the actual situation.

[0005] 2. Insufficient data fusion: Traditional technologies mostly rely on a single or simple multi-source data fusion method, which cannot effectively process heterogeneous data, resulting in insufficient expression of important features in the portrait.

[0006] 3. Low model training efficiency: Static training-based portrait models are difficult to adapt to real-time changes in expert characteristics. The introduction of new data requires complete retraining, which consumes a lot of time and computing resources.

[0007] 4. Low recommendation accuracy: Since it is impossible to dynamically adjust the weights of portrait features, the recommendation results based on portraits are often not targeted and cannot meet the diversity and real-time nature of user needs.

[0008] 5. Low storage and retrieval efficiency: Traditional portrait systems are prone to bottlenecks in the case of large-scale data storage and high concurrent access, resulting in low data access efficiency and inability to support efficient dynamic updates and real-time applications.

[0009] Therefore, how to provide a dynamic update method for expert portrait characterization 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 dynamic update method for expert portrait characterization. The present invention fully combines multi-source heterogeneous data fusion technology, online learning model, adaptive feature optimization algorithm, distributed storage architecture and intelligent recommendation engine, and describes in detail the technical process of dynamically capturing expert feature changes, updating portrait model in real time and generating accurate recommendation solutions. It has the advantages of strong timeliness, high accuracy and strong system adaptability.

[0011] A dynamic update method for expert portrait characterization according to an embodiment of the present invention is characterized by comprising the following steps:

[0012] S1. Build a multi-source heterogeneous data collection module to obtain experts' dynamic behavior data, knowledge graph association data, and interactive feedback data in real time through distributed collection nodes;

[0013] S2, through the multi-layer data fusion strategy, the collected heterogeneous data is embedded and represented, and the dynamic semantic feature extraction is realized by combining the context-aware model;

[0014] S3, using adaptive feature selection algorithm, assigning weight parameters according to real-time dynamic characteristics, and accurately optimizing the feature distribution of the portrait;

[0015] S4. Build a dynamic update model based on online learning, iteratively optimize model parameters through incremental training algorithms, and correct feature changes of expert portraits in real time;

[0016] S5. Apply the prediction and deduction mechanism, combine the historical feature change rate and current data, and generate a future trend model of expert portraits;

[0017] S6. Realize real-time storage and efficient access of portrait data through distributed storage architecture, dynamic concurrent update and retrieval functions;

[0018] S7. Integrated intelligent recommendation engine generates targeted recommendation solutions based on dynamically updated portraits and provides real-time feedback to the user decision-making system.

[0019] Optionally, the S1 specifically includes:

[0020] S11. Build a multi-source heterogeneous data acquisition module, including a dynamic behavior perception unit, a semantic association analysis unit, and a feedback data mining unit, to achieve real-time data acquisition and intelligent preprocessing through a distributed node cluster based on edge computing;

[0021] S12. In the dynamic behavior perception unit, the high-frequency data capture algorithm is used to monitor the expert's activity trajectory in real time. The behavior feature vector is represented by V b =f(t,a,c), where t is the time series, a is the behavior category, and c is the contextual semantic feature;

[0022] S13. In the semantic association analysis unit, based on the multi-hop path reasoning mechanism of the knowledge graph, the semantic network representation of experts and associated entities is dynamically generated, and the weight matrix is ​​W = [w ij ], where w ij represents the semantic association strength between the expert and entity j;

[0023] S14. In the feedback data mining unit, the deep feature learning model is combined to extract implicit feedback features from the interaction between users and experts to generate the interaction behavior feature set I f ={i 1 ,i 2 ,...,i p}, where i p Represents the embedded representation of the p-th interaction behavior;

[0024] S15, apply the distributed data task allocation algorithm to dynamically allocate data collection tasks to different collection nodes, and adopt the weighted synchronization mechanism T sync =αT 1 +βT 2 , where T 1 Indicates the acquisition delay, T 2 represents processing delay, α and β are weight parameters;

[0025] S16. Build a data integrity verification process. Through the two-way verification mechanism between the edge nodes and the central storage platform, dynamic behavior data, semantic association data, and feedback data are kept consistent during uploading and storage. Through the fusion and dynamic optimization of multi-source heterogeneous data, a high-dimensional data representation space is generated.

[0026] Optionally, the S2 specifically includes:

[0027] S21. Build a strategy framework based on multi-layer data fusion, divide the collected multi-source heterogeneous data into structured data, semi-structured data and unstructured data, and implement standardized preprocessing of data features through heterogeneous analysis modules;

[0028] S22, use the feature embedding generation algorithm to perform high-dimensional feature mapping on the structured data and generate a feature vector set F s ={f s1 ,f s2 ,...,f sn};

[0029] S23, using a hierarchical recursive nested model for semi-structured data, generating a dynamic embedding representation E through multi-layer feature association analysis ss ={e ss1 ,e ss2 ,...,e ssp}, and use feature screening algorithm to filter redundant features;

[0030] S24. For unstructured data, a semantic feature extraction method based on multimodal deep learning is used to generate a feature vector E through a joint visual-speech-text embedding model. us ={e us1 ,e us2 ,...,e usq}, realize the fusion of cross-modal features;

[0031] S25, using the fusion function based on adaptive weight allocation, the structured data feature F s , semi-structured embedding vector E ss and the unstructured embedding vector E us Fusion into a unified feature representation H(x) = α 1 F s +α 2 E ss +α 3 E us , where α 1 ,α 2 ,α 3 Optimize and adjust through dynamic weight allocation mechanism;

[0032] S26. Apply a context-aware dynamic dimensionality reduction algorithm to the fusion result to generate a multi-dimensional semantic feature vector V d = {v 1 ,v 2 ,...,v k}, and adjust the dimension k after dimensionality reduction according to the data distribution characteristics;

[0033] S27, the semantic feature vector V d Input the dynamic semantic parsing module, combined with the real-time context semantic reasoning model, to generate the embedded feature set S of the expert portrait c =φ(V d ,C t ), where C t is the current context semantic environment, φ represents the semantic reasoning function;

[0034] S28. Store the generated embedded feature set in the portrait feature database to provide accurate input for dynamically updating the model, and perform real-time feature retrieval and optimization.

[0035] Optionally, the S3 specifically includes:

[0036] S31, the fused feature data set X = {x 1 ,x 2 ,,x n} to conduct multi-dimensional analysis and use the real-time dynamic feature extraction model to construct a feature evaluation index set M = {m 1 ,m 2 ,...,m k}, where m i Represents feature x i The dynamic relevance, timeliness and predictive value of

[0037] S32. Design a dynamic weight allocation function and calculate the feature weight factor w through a multi-objective optimization method i :

[0038]

[0039] Where R(x i ) represents the feature x i The real-time correlation, T(x i ) represents the time sensitivity of the feature, λ 1 , 2 is the dynamic adjustment coefficient;

[0040] S33, based on weight factor w i Select the top k features in importance and generate the initial optimized feature set X opt ={x i |w i >θ,i∈[1,n]}, where θ is a dynamic threshold, which is updated in real time according to the characteristic distribution characteristics;

[0041] S34, applying the multi-dimensional feature interaction model, calculates the feature mutual information value I(x i ,x j ), construct the feature interaction matrix M inter , a simplified feature set X is generated by the feature combination optimization strategy that maximizes the mutual information gain refined ;

[0042] S35, using a dynamic optimization algorithm based on reinforcement learning to iteratively update the feature weight factor w' i :

[0043] w' i =w i +η·F(x i ,C t );

[0044] Where η is the learning rate, F(x i ,C t ) represents the feature x i In context C t The contribution value of

[0045] S36, generate the final optimized feature set X final ={x i |w' i >θ',i∈[1,n]} and input it into the expert portrait dynamic update model to complete the precise optimization of feature distribution and real-time update.

[0046] Optionally, the S4 specifically includes:

[0047] S41. Construct a dynamic update model based on online learning and define the parameter set Θ = θ 1 ,θ 2 ,...,θ n} and image feature mapping function g(F t ,Θ), initialize the parameters to generate the initial portrait model M 0 (Θ);

[0048] S42. Design a real-time update algorithm based on incremental learning to update the real-time streaming data D t =d t1 ,d t2 ,...,d tm} Dynamically update the model by batch input and use batch increment strategy to generate feature subset F t ={f t1 ,f t2 ,...,f tk};

[0049] S43, based on real-time feature subset F t , calculate the difference matrix ΔM = {m ij}, where m ij =|f ti -θ j | represents the deviation of features from parameters;

[0050] S44. Update parameters using optimized loss function:

[0051]

[0052] in w i is the dynamically adjusted feature weight, η is the learning rate;

[0053] S45, based on the updated model parameter Θ t+1 , the updated image feature vector V is generated through the dynamic image correction mechanism t =g(F t ,Θ t+1 ), and calculate the characteristic change rate ΔV=|V t -V t-1 |;

[0054] S46, introduce dynamic feature filtering and priority adjustment algorithm, amplify the weight of features with feature change rate higher than threshold δ, and generate optimized feature representation V opt = {v opt1 ,v opt2 ,...,v optk};

[0055] S47, using the updated portrait model M t+1 (Θ) Realize dynamic correction of expert portraits and store optimization results through a distributed storage architecture.

[0056] Optionally, the S5 specifically includes:

[0057] S51, extract the historical feature data set H of the expert portrait = {h 1 ,h 2 ,...,h n} and the corresponding time series T = {t 1 ,t 2 ,...,t n}, through nonlinear feature change modeling, construct the feature change rate function C(h i ,t i ):

[0058]

[0059] Where f(h i ,t i ) represents the nonlinear change of the feature over time, t i Indicates a point in time;

[0060] S52, using the characteristic change rate function C(h i ,t i ) and the real-time feature set F t ={f 1 ,f 2 ,...,f k}, construct a feature mapping model based on dynamic association deduction to generate the current change trend vector V t =g(C,F t ), where g represents the correlation function;

[0061] S53, through the time series recursive network combined with the dynamic characteristics of expert portraits, predict the future time T future =t n+1 ,t n+2 ,...,t n+m Characteristic trend of

[0062] P(h t+Δt)=f(V t ,C(h i ,t i ))+Δt·φ(V t ,Δt);

[0063] Where φ represents the time-dynamic change increment model based on deep learning;

[0064] S54. Forecast trend data The prediction error ∈ is corrected using a multivariate optimization algorithm:

[0065] P′(h t+Δt )=P(h t+Δt )+∈(V t ,T);

[0066] Where ∈(V t ,T) is the error correction factor, which is jointly determined by historical data and current dynamic characteristics;

[0067] S55. Combined with optimized future trend feature set Generate expert portrait future trend model M future (T), model parameters are updated in real time through feature dynamic fusion algorithm;

[0068] S56, the generated future trend model M future (T) is stored in a distributed storage system and provides a real-time interface for visual analysis and dynamic decision-making of future feature trends.

[0069] Optionally, the S6 specifically includes:

[0070] S61, construct a distributed storage architecture, including a distributed storage node set N = {n 1 ,n 2 ,...,n m and the central coordination node C 0 , using the consistent hashing algorithm to convert the portrait data D t Distribute to the optimal node according to data characteristics;

[0071] S62, adopt a real-time storage mechanism based on metadata index to store expert portrait data D t =d t1 ,d t2 ,...,d tk}According to timestamp T={t 1 ,t 2 ,...,t k} and data category label L = {l 1 ,l 2 ,...,l k Generate dynamic multidimensional index It =f(T,L), stored in a distributed set of nodes;

[0072] S63. Adopt an adaptive dynamic concurrent update mechanism, combined with distributed conflict detection and lock management algorithms, so that the data updated by multiple users can be updated in real time and remain consistent, where the constraints are:

[0073]

[0074] Where W(n i ) and R(n i ) represent the write operation and read operation set respectively, and t represents the operation delay;

[0075] S64, using cross-node retrieval algorithm, decomposing Q through query plan g = {Q(n 1 ),Q(n 2 ),...,Q(n m Distribute query requests to each storage node and implement low-latency retrieval based on the global result aggregation model:

[0076]

[0077] Where R(n i ) is the single node query result;

[0078] S65, using an improved node load balancing algorithm, with the node state matrix S(n i )=[u i ,l i ,a i ] is used to dynamically adjust storage allocation and access paths, where u i represents the node utilization, l i Indicates the current load, a i Indicates available resources;

[0079] S66. Use a distributed multi-version control mechanism to record the update history of the portrait data of each node, and use the timestamp chain V = {v 1 ,v 2 ,...,v p}Implement data version management and rollback;

[0080] S67. Integrate the distributed storage architecture with the dynamic update module to enable efficient real-time storage of portrait data, concurrent updates across nodes, and low-latency retrieval.

[0081] Optionally, the S7 specifically includes:

[0082] S71. Build an intelligent recommendation engine architecture, including a dynamic portrait input module, a recommendation strategy optimization module, a real-time feedback processing module, and a result push module. The dynamically updated portrait feature set V opt =v 1 ,v 2 ,...,v n};

[0083] S72. In the recommendation strategy optimization module, a recommendation strategy generation method based on multi-objective optimization is used to construct a recommendation function S(x, W, C), and the weight matrix W=[w ij ]:

[0084]

[0085] Where X is the candidate recommendation set, C is the context variable, R(x) represents the personalized score of the recommendation result, and α and β are dynamic adjustment coefficients;

[0086] S73. Collect user interaction data on recommendation results through the real-time feedback processing module. R =r 1 ,r 2 ,...,r k}, including click behavior, dwell time, selection rate and user rating, and real-time update of the feedback matrix;

[0087] S74. In the process of optimizing the recommendation strategy, the feedback data F R And dynamic image feature V opt , use the deep reinforcement learning model to optimize the recommendation weight W:

[0088]

[0089] Where η is the learning rate, L(F R ,P) represents the recommendation loss function, which is dynamically adjusted through real-time feedback data;

[0090] S75. In the result push module, the optimized recommendation function S'(x, W', C) is combined to generate the final recommendation solution set P final ={p 1 ,p 2 ,...,p l}, and push it to the user decision-making system in real time based on the user portrait;

[0091] S76. Introduce a visual analysis interface into the recommendation engine to display the generation logic of the recommendation strategy and the optimization process of user feedback, while dynamically adjusting the recommendation parameters.

[0092] The beneficial effects of the present invention are:

[0093] (1) The present invention provides deep analysis and dynamic adaptation capabilities for expert portrait features by combining multi-source heterogeneous data fusion technology, adaptive feature optimization algorithm and online learning model, so that the system can capture changes in expert features in real time, overcoming the defect that traditional static portrait models are difficult to reflect dynamic states, thereby significantly improving the timeliness and accuracy of portraits, especially in rapidly changing application scenarios.

[0094] (2) The present invention achieves efficient real-time storage and low-latency retrieval of portrait data through a distributed storage architecture and an optimized cross-node retrieval algorithm, supporting large-scale concurrent access and dynamic updates. This not only reduces the data access latency of the system, but also improves the efficiency and reliability of processing dynamically updated data.

[0095] (3) The present invention combines an intelligent recommendation engine with a deep learning model to generate accurate recommendation solutions through dynamically updated portrait data, and continuously optimizes the recommendation strategy based on real-time feedback. This method can not only meet the diverse needs of users, but also significantly improve the accuracy of recommendation results and the adaptability of the system, providing an efficient and reliable solution for decision support systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] 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:

[0097] Figure 1 This is a general framework diagram of a dynamic updating method for expert portrait characterization proposed by the present invention;

[0098] Figure 2 This is a data processing flow chart of a dynamic update method for expert portrait characterization proposed by the present invention. DETAILED DESCRIPTION

[0099] 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.

[0100] refer to Figure 1-2 , a dynamic updating method for expert portrait characterization, comprising the following steps:

[0101] S1. Build a multi-source heterogeneous data collection module to obtain experts' dynamic behavior data, knowledge graph association data, and interactive feedback data in real time through distributed collection nodes;

[0102] In this implementation, S1 specifically includes:

[0103] S11. Build a multi-source heterogeneous data acquisition module, including a dynamic behavior perception unit, a semantic association analysis unit, and a feedback data mining unit, to achieve real-time data acquisition and intelligent preprocessing through a distributed node cluster based on edge computing;

[0104] S12. In the dynamic behavior perception unit, the high-frequency data capture algorithm is used to monitor the expert's activity trajectory in real time. The behavior feature vector is represented by V b =f(t,a,c), where t is the time series, a is the behavior category, and c is the contextual semantic feature;

[0105] S13. In the semantic association analysis unit, based on the multi-hop path reasoning mechanism of the knowledge graph, the semantic network representation of experts and associated entities is dynamically generated, and the weight matrix is ​​W = [w ij ], where w ij represents the semantic association strength between the expert and entity j;

[0106] S14. In the feedback data mining unit, the deep feature learning model is combined to extract implicit feedback features from the interaction between users and experts to generate the interaction behavior feature set I f ={i 1 ,i 2 ,...,i p}, where i p Represents the embedded representation of the p-th interaction behavior;

[0107] S15, apply the distributed data task allocation algorithm to dynamically allocate data collection tasks to different collection nodes, and adopt the weighted synchronization mechanism T sync =αT 1 +βT 2 , where T 1 Indicates the acquisition delay, T 2 represents processing delay, α and β are weight parameters;

[0108] S16. Build a data integrity verification process. Through the two-way verification mechanism between the edge nodes and the central storage platform, dynamic behavior data, semantic association data, and feedback data are kept consistent during uploading and storage. Through the fusion and dynamic optimization of multi-source heterogeneous data, a high-dimensional data representation space is generated.

[0109] S2, through the multi-layer data fusion strategy, the collected heterogeneous data is embedded and represented, and the dynamic semantic feature extraction is realized by combining the context-aware model;

[0110] In this implementation, S2 specifically includes:

[0111] S21. Build a strategy framework based on multi-layer data fusion, divide the collected multi-source heterogeneous data into structured data, semi-structured data and unstructured data, and implement standardized preprocessing of data features through heterogeneous analysis modules;

[0112] S22, use the feature embedding generation algorithm to perform high-dimensional feature mapping on the structured data and generate a feature vector set F s ={f s1 ,f s2 ,...,f sn};

[0113] S23, using a hierarchical recursive nested model for semi-structured data, generating a dynamic embedding representation E through multi-layer feature association analysis ss ={e ss1 ,e ss2 ,...,e ssp}, and use feature screening algorithm to filter redundant features;

[0114] S24. For unstructured data, a semantic feature extraction method based on multimodal deep learning is used to generate a feature vector E through a joint visual-speech-text embedding model. us ={e us1 ,e us2 ,...,e usq}, realize the fusion of cross-modal features;

[0115] S25, using the fusion function based on adaptive weight allocation, the structured data feature F s , semi-structured embedding vector E ss and the unstructured embedding vector E us Fusion into a unified feature representation H(x) = α 1 F s +α 2 E ss +α 3 E us , where α 1 ,α 2 ,α 3 Optimize and adjust through dynamic weight allocation mechanism;

[0116] S26. Apply a context-aware dynamic dimensionality reduction algorithm to the fusion result to generate a multi-dimensional semantic feature vector V d = {v 1 ,v 2 ,...,v k}, and adjust the dimension k after dimensionality reduction according to the data distribution characteristics;

[0117] S27, the semantic feature vector V dInput the dynamic semantic parsing module, combined with the real-time context semantic reasoning model, to generate the embedded feature set S of the expert portrait c =φ(V d ,C t ), where C t is the current context semantic environment, φ represents the semantic reasoning function;

[0118] S28. Store the generated embedded feature set in the portrait feature database to provide accurate input for dynamically updating the model, and perform real-time feature retrieval and optimization.

[0119] S3, using adaptive feature selection algorithm, assigning weight parameters according to real-time dynamic characteristics, and accurately optimizing the feature distribution of the portrait;

[0120] In this implementation, S3 specifically includes:

[0121] S31, the fused feature data set X = {x 1 ,x 2 ,,x n} to conduct multi-dimensional analysis and use the real-time dynamic feature extraction model to construct a feature evaluation index set M = {m 1 ,m 2 ,...,m k}, where m i Represents feature x i The dynamic relevance, timeliness and predictive value of

[0122] S32. Design a dynamic weight allocation function and calculate the feature weight factor w through a multi-objective optimization method i :

[0123]

[0124] Where R(x i ) represents the feature x i The real-time correlation, T(x i ) represents the time sensitivity of the feature, λ 1 , 2 is the dynamic adjustment coefficient;

[0125] S33, based on weight factor w i Select the top k features in importance and generate the initial optimized feature set X opt ={x i |w i >θ,i∈[1,n]}, where θ is a dynamic threshold, which is updated in real time according to the characteristic distribution characteristics;

[0126] S34, applying the multi-dimensional feature interaction model, calculates the feature mutual information value I(x i,x j ), construct the feature interaction matrix M inter , a simplified feature set X is generated by the feature combination optimization strategy that maximizes the mutual information gain refined ;

[0127] S35, using a dynamic optimization algorithm based on reinforcement learning to iteratively update the feature weight factor w' i :

[0128] w' i =w i +η·F(x i ,C t );

[0129] Where η is the learning rate, F(x i ,C t ) represents the feature x i In context C t The contribution value of

[0130] S36, generate the final optimized feature set X final ={x i |w' i >θ',i∈[1,n]} and input it into the expert portrait dynamic update model to complete the precise optimization of feature distribution and real-time update.

[0131] S4. Build a dynamic update model based on online learning, iteratively optimize model parameters through incremental training algorithms, and correct feature changes of expert portraits in real time;

[0132] In this implementation, S4 specifically includes:

[0133] S41. Construct a dynamic update model based on online learning and define the parameter set Θ = θ 1 ,θ 2 ,...,θ n} and image feature mapping function g(F t ,Θ), initialize the parameters to generate the initial portrait model M 0 (Θ);

[0134] S42. Design a real-time update algorithm based on incremental learning to update the real-time streaming data D t =d t1 ,d t2 ,...,d tm} Dynamically update the model by batch input and use batch increment strategy to generate feature subset F t ={f t1 ,f t2 ,...,f tk};

[0135] S43, based on real-time feature subset F t , calculate the difference matrix ΔM = {m ij}, where m ij =|f ti -θ j | represents the deviation of features from parameters;

[0136] S44. Update parameters using optimized loss function:

[0137]

[0138] in w i is the dynamically adjusted feature weight, η is the learning rate;

[0139] S45, based on the updated model parameter Θ t+1 , the updated image feature vector V is generated through the dynamic image correction mechanism t =g(F t ,Θ t+1 ), and calculate the characteristic change rate ΔV=|V t -V t-1 |;

[0140] S46, introduce dynamic feature filtering and priority adjustment algorithm, amplify the weight of features with feature change rate higher than threshold δ, and generate optimized feature representation V opt = {v opt1 ,v opt2 ,...,v optk};

[0141] S47, using the updated portrait model M t+1 (Θ) Realize dynamic correction of expert portraits and store optimization results through a distributed storage architecture.

[0142] S5. Apply the prediction and deduction mechanism, combine the historical feature change rate and current data, and generate a future trend model of expert portraits;

[0143] In this implementation, S5 specifically includes:

[0144] S51, extract the historical feature data set H of the expert portrait = {h 1 ,h 2 ,...,h n} and the corresponding time series T = {t 1 ,t 2 ,...,t n}, through nonlinear feature change modeling, construct the feature change rate function C(h i ,t i ):

[0145]

[0146] Where f(h i ,t i ) represents the nonlinear change of the feature over time, t i Indicates a point in time;

[0147] S52, using the characteristic change rate function C(h i ,t i ) and the real-time feature set F t ={f 1 ,f 2 ,...,f k}, construct a feature mapping model based on dynamic association deduction to generate the current change trend vector V t =g(C,F t ), where g represents the correlation function;

[0148] S53, through the time series recursive network combined with the dynamic change characteristics of expert portraits, predict the future time T future ={t n+1 ,t n+2 ,...,t n+m Characteristic trend of

[0149] P(h t+Δt )=f(V t ,C(h i ,t i ))+Δt·φ(V t ,Δt);

[0150] Where φ represents the time-dynamic change increment model based on deep learning;

[0151] S54. Forecast trend data The prediction error ∈ is corrected using a multivariate optimization algorithm:

[0152] P′(h t+Δt )=P(h t+Δt )+∈(V t ,T);

[0153] Where ∈(V t ,T) is the error correction factor, which is jointly determined by historical data and current dynamic characteristics;

[0154] S55. Combined with optimized future trend feature set Generate expert portrait future trend model M future (T), model parameters are updated in real time through feature dynamic fusion algorithm;

[0155] S56, the generated future trend model M future (T) is stored in a distributed storage system and provides a real-time interface for visual analysis and dynamic decision-making of future feature trends.

[0156] S6. Realize real-time storage and efficient access of portrait data through distributed storage architecture, dynamic concurrent update and retrieval functions;

[0157] In this implementation, S6 specifically includes:

[0158] S61, construct a distributed storage architecture, including a distributed storage node set N = {n 1 ,n 2 ,...,n m and the central coordination node C 0 , using the consistent hashing algorithm to convert the portrait data D t Distribute to the optimal node according to data characteristics;

[0159] S62, adopt a real-time storage mechanism based on metadata index to store expert portrait data D t =d t1 ,d t2 ,...,d tk}According to timestamp T={t 1 ,t 2 ,...,t k} and data category label L = {l 1 ,l 2 ,...,l k Generate dynamic multidimensional index I t =f(T,L), stored in a distributed set of nodes;

[0160] S63. Adopt an adaptive dynamic concurrent update mechanism, combined with distributed conflict detection and lock management algorithms, so that the data updated by multiple users can be updated in real time and remain consistent, where the constraints are:

[0161]

[0162] Where W(n i ) and R(n i ) represent the write operation and read operation set respectively, and t represents the operation delay;

[0163] S64, using cross-node retrieval algorithm, decomposing Q through query plan g = {Q(n 1 ),Q(n 2 ),...,Q(n m Distribute query requests to each storage node and implement low-latency retrieval based on the global result aggregation model:

[0164]

[0165] Where R(n i ) is the single node query result;

[0166] S65, using an improved node load balancing algorithm, with the node state matrix S(n i )=[u i ,l i ,a i ] is used to dynamically adjust storage allocation and access paths, where u i represents the node utilization, l i Indicates the current load, a i Indicates available resources;

[0167] S66. Use a distributed multi-version control mechanism to record the update history of the portrait data of each node, and use the timestamp chain V = {v 1 ,v 2 ,...,v p}Implement data version management and rollback;

[0168] S67. Integrate the distributed storage architecture with the dynamic update module to enable efficient real-time storage of portrait data, concurrent updates across nodes, and low-latency retrieval.

[0169] S7. Integrated intelligent recommendation engine generates targeted recommendation solutions based on dynamically updated portraits and provides real-time feedback to the user decision-making system.

[0170] In this implementation, S7 specifically includes:

[0171] S71. Build an intelligent recommendation engine architecture, including a dynamic portrait input module, a recommendation strategy optimization module, a real-time feedback processing module, and a result push module. The dynamically updated portrait feature set V opt =v 1 ,v 2 ,...,v n};

[0172] S72. In the recommendation strategy optimization module, a recommendation strategy generation method based on multi-objective optimization is used to construct a recommendation function S(x, W, C), and the weight matrix W=[w ij ]:

[0173]

[0174] Where X is the candidate recommendation set, C is the context variable, R(x) represents the personalized score of the recommendation result, and α and β are dynamic adjustment coefficients;

[0175] S73. Collect user interaction data on recommendation results through the real-time feedback processing module. R =r 1 ,r 2 ,...,r k}, including click behavior, dwell time, selection rate and user rating, and real-time update of the feedback matrix;

[0176] S74. In the process of optimizing the recommendation strategy, the feedback data F R And dynamic image feature V opt , use the deep reinforcement learning model to optimize the recommendation weight W:

[0177]

[0178] Where η is the learning rate, L(F R ,P) represents the recommendation loss function, which is dynamically adjusted through real-time feedback data;

[0179] S75. In the result push module, the optimized recommendation function S'(x, W', C) is combined to generate the final recommendation solution set P final ={p 1 ,p 2 ,...,p l}, and push it to the user decision-making system in real time based on the user portrait;

[0180] S76. Introduce a visual analysis interface into the recommendation engine to display the generation logic of the recommendation strategy and the optimization process of user feedback, while dynamically adjusting the recommendation parameters.

[0181] Embodiment 1:

[0182] In order to verify the feasibility of the present invention, the present invention is applied to the expert database system of an educational institution B. Institution B has a large amount of expert data, covering academic, technical and management fields, but due to the static update method of expert portraits, the effect of its system in intelligent recommendation and dynamic analysis is not satisfactory, especially the accuracy of the recommendation scheme and the timeliness of expert characteristics are seriously affected. To solve these problems, institution B decided to deploy a dynamic update method for expert portrait characterization of the present invention.

[0183] After applying the present invention, the system first collects the behavioral data and interaction records of experts in academic platforms, technical communities and internal management systems in real time through a multi-source heterogeneous data collection module. After cleaning, denoising and standardization, these data are input into the data fusion module, and the expert portrait features are optimized using a dynamic weight allocation algorithm to generate a high-dimensional feature vector. The system adopts a dynamic update model based on online learning, updates the model parameters in real time through incremental training, and captures the dynamic change trend of the expert portrait. After completing the portrait update, the intelligent recommendation engine combines the updated portrait data to generate a personalized recommendation plan, and realizes efficient data storage and retrieval through a distributed storage architecture.

[0184] In the specific application scenario, the system ran for a month to support the academic resource allocation decision of Institution B. Through dynamically updated expert portraits, the intelligent recommendation engine can recommend the most suitable experts according to the current project needs, while capturing the changes in the expert's research direction, project participation and cooperation network in real time. For example, when a scientific research project was forming a team, the system recommended an expert whose recent research direction shifted to a project-related field. After the recommendation was adopted, the success rate of the project team was significantly improved.

[0185] In order to evaluate the effect of the present invention, institution B compared the performance of the expert profiling system before and after deployment. The specific data are shown in Table 1:

[0186] Table 1B Comparison of performance data of institutional expert portrait system

[0187]

[0188]

[0189] It can be seen from the data in Table 1 that the dynamic update method of the present invention significantly improves the performance of the expert portrait system. For example, the portrait update is increased from once a month to real-time update, the recommendation accuracy is increased from 67.4% to 92.8%, and the data processing delay and recommendation response time are shortened from 45 minutes and 60 minutes to 5 minutes and 10 minutes respectively. In addition, the system's data storage efficiency is increased by 4 times, and the data redundancy rate is reduced to 3%. In actual decision support, the rate of effective support obtained through the portrait system in decision-making has increased from 65.7% to 94.5%, which greatly improves the value of the system in practical applications.

[0190] In a specific scenario, an academic resource allocation decision needed to match an expert who was good at "artificial intelligence and ethics research". The system captured the recent high-frequency activities of an expert in this field through dynamic portraits, and recommended him to the decision-making team based on the weight allocation mechanism of the intelligent recommendation engine. In the end, the recommendation was adopted and proved to be extremely effective, significantly improving the academic influence of the project.

[0191] The present invention realizes efficient dynamic updating of expert portraits and significant improvement of application effects by dynamically capturing changes in expert characteristics, updating portrait models in real time, and accurately matching intelligent recommendation engines. It not only solves the problems of delayed updating, low recommendation accuracy, and poor storage efficiency in traditional portrait technology, but also provides reliable decision-making support for institution B, and improves the efficiency and intelligence level of academic resource allocation.

[0192] 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 dynamic updating method for expert portrait characterization, characterized in that: The steps include: S1. Build a multi-source heterogeneous data collection module to obtain experts' dynamic behavior data, knowledge graph association data, and interactive feedback data in real time through distributed collection nodes; S2, through the multi-layer data fusion strategy, the collected heterogeneous data is embedded and represented, and the dynamic semantic feature extraction is realized by combining the context-aware model; S3, using adaptive feature selection algorithm, assigning weight parameters according to real-time dynamic characteristics, and accurately optimizing the feature distribution of the portrait; S4. Build a dynamic update model based on online learning, iteratively optimize model parameters through incremental training algorithms, and correct feature changes of expert portraits in real time; S5. Apply the prediction and deduction mechanism, combine the historical feature change rate and current data, and generate a future trend model of the expert portrait; S6. Realize real-time storage and efficient access of portrait data through distributed storage architecture, dynamic concurrent update and retrieval functions; S7. Integrate intelligent recommendation engine to generate targeted recommendation solutions based on dynamically updated portraits and provide real-time feedback to the user decision-making system.

2. A dynamic updating method for expert portrait characterization according to claim 1, characterized in that: The S1 specifically includes: S11. Build a multi-source heterogeneous data acquisition module, including a dynamic behavior perception unit, a semantic association analysis unit, and a feedback data mining unit, to achieve real-time data acquisition and intelligent preprocessing through a distributed node cluster based on edge computing; S12. In the dynamic behavior perception unit, the high-frequency data capture algorithm is used to monitor the expert's activity trajectory in real time. The behavior feature vector is represented by V b =f(t,a,c), where t is the time series, a is the behavior category, and c is the contextual semantic feature; S13. In the semantic association analysis unit, based on the multi-hop path reasoning mechanism of the knowledge graph, the semantic network representation of experts and associated entities is dynamically generated, and the weight matrix is ​​W = [w ij ], where w ij represents the semantic association strength between the expert and entity j; S14. In the feedback data mining unit, the deep feature learning model is combined to extract implicit feedback features from the interaction between users and experts to generate the interaction behavior feature set I f ={i1,i2,...,i p }, where i p Represents the embedded representation of the p-th interaction behavior; S15, apply the distributed data task allocation algorithm to dynamically allocate data collection tasks to different collection nodes, and adopt the weighted synchronization mechanism T sync =αT1+βT2, where T1 represents acquisition delay, T2 represents processing delay, and α and β are weight parameters; S16. Build a data integrity verification process. Through the two-way verification mechanism between the edge nodes and the central storage platform, dynamic behavior data, semantic association data, and feedback data are kept consistent during uploading and storage. Through the fusion and dynamic optimization of multi-source heterogeneous data, a high-dimensional data representation space is generated.

3. A dynamic updating method for expert portrait characterization according to claim 1, characterized in that: The S2 specifically includes: S21. Build a strategy framework based on multi-layer data fusion, divide the collected multi-source heterogeneous data into structured data, semi-structured data and unstructured data, and implement standardized preprocessing of data features through heterogeneous analysis modules; S22, use the feature embedding generation algorithm to perform high-dimensional feature mapping on the structured data and generate a feature vector set F s ={f s1 ,f s2 ,...,f sn }; S23, using a hierarchical recursive nested model for semi-structured data, generating a dynamic embedding representation E through multi-layer feature association analysis ss ={e ss1 ,e ss2 ,...,e ssp }, and use feature screening algorithm to filter redundant features; S24. For unstructured data, a semantic feature extraction method based on multimodal deep learning is used to generate a feature vector E through a joint visual-speech-text embedding model. us ={e us1 ,e us2 ,...,e usq }, realize the fusion of cross-modal features; S25, using the fusion function based on adaptive weight allocation, the structured data feature F s , semi-structured embedding vector E ss and the unstructured embedding vector E us Fusion into a unified feature representation H(x) = α1F s +α2E ss +α3E us , where α1, α2, and α3 are optimized and adjusted through a dynamic weight allocation mechanism; S26. Apply a context-aware dynamic dimensionality reduction algorithm to the fusion result to generate a multi-dimensional semantic feature vector V d ={v1,v2,...,v k }, and adjust the dimension k after dimensionality reduction according to the data distribution characteristics; S27, the semantic feature vector V d Input the dynamic semantic parsing module, combined with the real-time context semantic reasoning model, to generate the embedded feature set S of the expert portrait c =φ(V d ,C t ), where C t is the current context semantic environment, φ represents the semantic reasoning function; S28. Store the generated embedded feature set in the portrait feature database to provide accurate input for dynamically updating the model, and perform real-time feature retrieval and optimization.

4. A dynamic updating method for expert portrait characterization according to claim 1, characterized in that: The S3 specifically includes: S31, for the fused feature data set X = {x1, x2,, x n } to conduct multi-dimensional analysis and use the real-time dynamic feature extraction model to construct a feature evaluation index set M = {m1,m2,...,m k }, where m i Represents feature x i The dynamic relevance, timeliness and predictive value of S32. Design a dynamic weight allocation function and calculate the feature weight factor w through a multi-objective optimization method i : Where R(x i ) represents the feature x i The real-time correlation, T(x i ) represents the time sensitivity of the feature, λ1 and λ2 are dynamic adjustment coefficients; S33, based on weight factor w i Select the top k features in importance and generate the initial optimized feature set X opt ={x i |w i >θ,i∈[1,n]}, where θ is a dynamic threshold, which is updated in real time according to the characteristic distribution characteristics; S34, applying the multi-dimensional feature interaction model, calculates the feature mutual information value I(x i ,x j ), construct the feature interaction matrix M inter , a simplified feature set X is generated by the feature combination optimization strategy that maximizes the mutual information gain refined ; S35, using a dynamic optimization algorithm based on reinforcement learning to iteratively update the feature weight factor w' i : w' i =w i +η·F(x i ,C t ); Where η is the learning rate, F(x i ,C t ) represents the feature x i In context C t The contribution value of S36, generate the final optimized feature set X final ={x i |w' i >θ',i∈[1,n]} and input it into the expert portrait dynamic update model to complete the precise optimization of feature distribution and real-time update.

5. A dynamic updating method for expert portrait characterization according to claim 1, characterized in that: The S4 specifically includes: S41. Construct a dynamic update model based on online learning and define the parameter set Θ = θ1, θ2, ..., θ n } and image feature mapping function g(F t ,Θ), initialize parameters to generate the initial portrait model M0(Θ); S42. Design a real-time update algorithm based on incremental learning to update the real-time streaming data D t ={d t1 ,d t2 ,...,d tm } Dynamically update the model by batch input and use batch increment strategy to generate feature subset F t ={f t1 ,f t2 ,...,f tk }; S43, based on real-time feature subset F t , calculate the difference matrix ΔM = {m ij }, where m ij =|f ti -θ j | represents the deviation of features from parameters; S44. Update parameters using optimized loss function: in w i is the dynamically adjusted feature weight, η is the learning rate; S45, based on the updated model parameter Θ t+1 , the updated image feature vector V is generated through the dynamic image correction mechanism t =g(F t ,Θ t+1 ), and calculate the characteristic change rate ΔV=|V t -V t-1 |; S46, introduce dynamic feature filtering and priority adjustment algorithm, amplify the weight of features with feature change rate higher than threshold δ, and generate optimized feature representation V opt = {v opt1 ,v opt2 ,...,v optk }; S47, using the updated portrait model M t+1 (Θ) Realize dynamic correction of expert portraits and store optimization results through a distributed storage architecture.

6. A dynamic updating method for expert portrait characterization according to claim 1, characterized in that: The S5 specifically includes: S51, extract the historical feature data set H of the expert portrait = {h1,h2,...,h n } and the corresponding time series T = {t1, t2, ..., t n }, through nonlinear feature change modeling, construct the feature change rate function C(h i ,t i ): Where f(h i ,t i ) represents the nonlinear change of the feature over time, t i Indicates a point in time; S52, using the characteristic change rate function C(h i ,t i ) and the real-time feature set F t ={f1,f2,...,f k }, construct a feature mapping model based on dynamic association deduction to generate the current change trend vector V t =g(C,F t ), where g represents the correlation function; S53, through the time series recursive network combined with the dynamic change characteristics of expert portraits, predict the future time T future ={t n+1 ,t n+2 ,...,t n+m Characteristic trend of P(h t+Δt )=f(V t ,C(h i ,t i ))+Δt·φ(V t ,Δt); Where φ represents the time-dynamic change increment model based on deep learning; S54. Forecast trend data The prediction error ∈ is corrected using a multivariate optimization algorithm: P′(h t+Δt )=P(h t+Δt )+∈(V t ,T); Where ∈(V t ,T) is the error correction factor, which is jointly determined by historical data and current dynamic characteristics; S55. Combined with optimized future trend feature set Generate expert portrait future trend model M future (T), model parameters are updated in real time through feature dynamic fusion algorithm; S56, the generated future trend model M future (T) is stored in a distributed storage system and provides a real-time interface for visual analysis and dynamic decision-making of future feature trends.

7. A dynamic updating method for expert portrait characterization according to claim 1, characterized in that: The S6 specifically includes: S61, construct a distributed storage architecture, including a distributed storage node set N = {n1, n2, ..., n m } and the central coordination node C0, using the consistent hashing algorithm to convert the portrait data D t Distribute to the optimal node according to data characteristics; S62, adopt a real-time storage mechanism based on metadata index to store expert portrait data D t ={d t1 ,d t2 ,...,d tk }According to timestamp T={t1,t2,...,t k } and data category label L = {l1,l2,...,l k Generate dynamic multidimensional index I t =f(T,L), stored in a distributed set of nodes; S63. Adopt an adaptive dynamic concurrent update mechanism, combined with distributed conflict detection and lock management algorithms, so that the data updated by multiple users can be updated in real time and remain consistent, where the constraints are: if then enforce lock(n i ,t); Where W(n i ) and R(n i ) represent the write operation and read operation set respectively, and t represents the operation delay; S64, using cross-node retrieval algorithm, decomposing Q through query plan g = {Q(n1),Q(n2),...,Q(n m )}Distribute query requests to each storage node and implement low-latency retrieval based on the global result aggregation model: Where R(n i ) is the single node query result; S65, using an improved node load balancing algorithm, with the node state matrix S(n i )=[u i ,l i ,a i ] is used to dynamically adjust storage allocation and access paths, where u i represents the node utilization, l i Indicates the current load, a i Indicates available resources; S66, use a distributed multi-version control mechanism to record the update history of the portrait data of each node, through the timestamp chain V = {v1, v2, ..., v p }Implement data version management and rollback; S67. Integrate the distributed storage architecture with the dynamic update module to enable efficient real-time storage of portrait data, concurrent updates across nodes, and low-latency retrieval.

8. A dynamic updating method for expert portrait characterization according to claim 1, characterized in that: The S7 specifically includes: S71. Build an intelligent recommendation engine architecture, including a dynamic portrait input module, a recommendation strategy optimization module, a real-time feedback processing module, and a result push module. The dynamically updated portrait feature set V opt ={v1,v2,...,v n }; S72. In the recommendation strategy optimization module, a recommendation strategy generation method based on multi-objective optimization is used to construct a recommendation function S(x, W, C), and the weight matrix W=[w ij ]: Where X is the candidate recommendation set, C is the context variable, R(x) represents the personalized score of the recommendation result, and α and β are dynamic adjustment coefficients; S73. Collect user interaction data on recommendation results through the real-time feedback processing module. R ={r1,r2,...,r k }, including click behavior, dwell time, selection rate and user rating, and real-time update of the feedback matrix; S74. In the process of optimizing the recommendation strategy, the feedback data F R and dynamic image features V opt , use the deep reinforcement learning model to optimize the recommendation weight W: Where η is the learning rate, L(F R ,P) represents the recommendation loss function, which is dynamically adjusted through real-time feedback data; S75. In the result push module, the optimized recommendation function S'(x, W', C) is combined to generate the final recommendation solution set P final ={p1,p2,...,p l }, and push it to the user decision-making system in real time based on the user portrait; S76. Introduce a visual analysis interface into the recommendation engine to display the generation logic of the recommendation strategy and the optimization process of user feedback, while dynamically adjusting the recommendation parameters.

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