A dynamic update method for expert profiling

Abstract

This invention discloses a dynamic update method for expert profile characterization, comprising: S1, constructing a multi-source heterogeneous data acquisition module to acquire expert data in real time through distributed acquisition nodes; S2, embedding the acquired heterogeneous data into a representation and combining it with a context-aware model to achieve dynamic semantic feature extraction; S3, allocating weight parameters based on real-time dynamic characteristics to accurately optimize the feature distribution of the profile; S4, iteratively optimizing model parameters through an incremental training algorithm and correcting feature changes in the expert profile in real time; S5, generating a future trend model of the expert profile by combining historical feature change rates and current data; S6, achieving real-time storage and efficient access to profile data through a distributed storage architecture; and S7, generating targeted recommendation schemes based on the dynamically updated profiles and feeding them back to the user decision-making system in real time. This invention has the advantages of high timeliness, high accuracy, and strong system adaptability.

Description

Technical Field

[0001] This invention relates to the field of expert profiling technology, and in particular to a dynamic updating method for expert profiling. Background Technology

[0002] With the rapid development of artificial intelligence and big data technologies, expert profiling has become an important research direction in many fields. Constructing expert profiles can play a crucial role in knowledge management, intelligent recommendation, and resource allocation. However, existing expert profiling technologies still have many shortcomings in terms of dynamism and accuracy, making it difficult to meet rapidly changing industry needs and diverse application scenarios.

[0003] In existing technologies, traditional expert profiling methods are typically based on static data and fixed models. These methods have significant limitations when dealing with dynamic changes in expert characteristics and the need for real-time decision-making. Specifically, traditional methods have significant shortcomings in the following aspects:

[0004] 1. Lack of dynamic update capability: Existing expert profiles are mostly based on fixed-period data updates and model training, which cannot capture changes in expert characteristics in real time, resulting in profile results lagging behind the actual situation.

[0005] 2. Insufficient data fusion: Traditional technologies often rely on single or simple multi-source data fusion methods, which cannot effectively handle heterogeneous data, resulting in insufficient ability to express important features in the profile.

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

[0007] 4. Low recommendation accuracy: Because the weight of profile features cannot be dynamically adjusted, the recommendation results based on profiles often lack specificity and are difficult to meet the diversity and real-time needs of users.

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

[0009] Therefore, how to provide a dynamic updating method for expert profiling is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0010] One objective of this invention is to propose a dynamic update method for expert profile characterization. This invention fully integrates multi-source heterogeneous data fusion technology, online learning models, adaptive feature optimization algorithms, distributed storage architecture, and intelligent recommendation engines. It describes in detail the technical process of dynamically capturing changes in expert features, updating the profile model in real time, and generating accurate recommendation schemes. It has the advantages of strong timeliness, high accuracy, and strong system adaptability.

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

[0012] S1. Construct a multi-source heterogeneous data acquisition module to acquire dynamic behavioral data, knowledge graph association data, and interactive feedback data of experts in real time through distributed acquisition nodes;

[0013] S2. Through a multi-layer data fusion strategy, the collected heterogeneous data is embedded and combined with a context-aware model to achieve dynamic semantic feature extraction.

[0014] S3. Adaptive feature selection algorithm is adopted to allocate weight parameters according to real-time dynamic characteristics and accurately optimize the feature distribution of the portrait.

[0015] S4. Construct a dynamically updated model based on online learning, iteratively optimize model parameters through incremental training algorithms, and correct feature changes in expert profiles in real time.

[0016] S5. Apply the predictive inference mechanism, combine historical feature change rates and current data to generate a future trend model of expert profiles;

[0017] S6. Real-time storage and efficient access to profile data, dynamic concurrent update and retrieval functions are achieved through a distributed storage architecture;

[0018] S7 integrates an intelligent recommendation engine that generates targeted recommendation solutions based on dynamically updated user profiles and provides real-time feedback to the user's decision-making system.

[0019] Optionally, S1 specifically includes:

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

[0021] S12. In the dynamic behavior perception unit, a high-frequency data capture algorithm is used to monitor the expert's activity trajectory in real time, and the behavior feature vector is represented as 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 parsing unit, based on the multi-hop path reasoning mechanism of the knowledge graph, a semantic network representation of experts and related entities is dynamically generated, with the weight matrix being W = [w ij ], where w ij This indicates the strength of the semantic association between the expert and entity j;

[0023] S14. In the feedback data mining unit, implicit feedback features are extracted from the interaction behavior between users and experts by combining a deep feature learning model, generating an interaction behavior feature set I. f ={i1,i2,...,i p}, where i p The embedding representation of the p-th interaction behavior;

[0024] S15. Apply a distributed data task allocation algorithm to dynamically allocate data acquisition tasks to different acquisition nodes, and adopt a weighted synchronization mechanism T. sync =αT1+βT2, where T1 represents the acquisition delay, T2 represents the processing delay, and α and β are weight parameters;

[0025] S16. Construct a data integrity verification process. Through a two-way verification mechanism between edge nodes and the central storage platform, ensure that dynamic behavioral data, semantically related data, and feedback data remain consistent during uploading and storage. Generate a high-dimensional data representation space through the fusion and dynamic optimization of multi-source heterogeneous data.

[0026] Optionally, S2 specifically includes:

[0027] S21. Construct 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 realize standardized preprocessing of data features through the heterogeneous parsing module.

[0028] S22. Use a feature embedding generation algorithm to perform high-dimensional feature mapping on structured data, generating a set of feature vectors F. s ={f s1 ,f s2 ,...,f sn};

[0029] S23. For semi-structured data, a hierarchical recursive nested model is adopted to generate a dynamic embedded representation E through multi-level feature association analysis. ss ={e ss1 ,e ss2 ,...,e ssp}, and use a feature filtering algorithm to filter redundant features;

[0030] S24. For unstructured data, a semantic feature extraction method based on multimodal deep learning is adopted, and a feature vector E is generated through a vision-speech-text joint embedding model. us ={e us1 ,e us2 ,...,e usq This enables the fusion of cross-modal features.

[0031] S25. Employ a fusion function based on adaptive weight allocation to integrate the structured data features F. s Semi-structured embedding vector E ss Unstructured embedding vector E us The fusion is represented as a unified feature representation H(x) = α1F s +α2E ss +α3E us α1, α2, and α3 are optimized and adjusted through a 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 ={v1,v2,...,v k}, and adjust the dimension k after dimensionality reduction according to the data distribution characteristics;

[0033] S27. Transfer the semantic feature vector V d The input dynamic semantic parsing module, combined with the real-time contextual semantic reasoning model, generates the embedded feature set S of the expert profile. c =φ(V d C t ), where C t Given the current semantic context, φ represents the semantic inference function;

[0034] S28. Store the generated embedded feature set in the portrait feature database to provide accurate input for dynamically updating the model, while simultaneously retrieving and optimizing features in real time.

[0035] Optionally, S3 specifically includes:

[0036] S31. For the fused feature dataset X = {x1, x2, ..., x...} n A multi-dimensional analysis is performed, and a feature evaluation index set M = {m1, m2, ..., m} is constructed using a real-time dynamic feature extraction model. k}, where m i Indicates feature x i The dynamic correlation, timeliness, and predictive value of [the data / information].

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

[0038]

[0039] Where R(x) i ) represents feature x i Real-time correlation, T(x) i ) represents the time sensitivity of the feature, and λ1 and λ2 are dynamic adjustment coefficients;

[0040] S33, Based on weighting factor w i Select the top k features by importance to generate a preliminary optimized feature set X. opt ={x i |w i >θ,i∈[1,n]}, where θ is a dynamic threshold that is updated in real time based on the feature distribution characteristics;

[0041] S34. Apply the multidimensional feature interaction model to calculate the feature mutual information value I(x) in the preliminary optimized feature set. i ,x j Construct the feature interaction matrix M inter A simplified feature set X is generated by a feature combination optimization strategy that maximizes mutual information gain. refined ;

[0042] S35. Employ a reinforcement learning-based dynamic optimization algorithm 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 feature x i In context C t The contribution value below;

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

[0046] Optionally, S4 specifically includes:

[0047] S41. Construct a dynamically updated model based on online learning, defining the parameter set Θ = θ1, θ2, ..., θ n} and the portrait feature mapping function g(Ft ,Θ), initialize parameters to generate initial portrait model M0(Θ);

[0048] S42. Design a real-time update algorithm based on incremental learning to update real-time streaming data D. t =d t1 ,d t2 ,...,d tm The model is dynamically updated by inputting data in batches, and a feature subset F is generated using a batch incremental strategy. t ={f t1 ,f t2 ,...,f tk};

[0049] S43, Based on real-time feature subset F t Calculate the difference matrix ΔM = {m} with the current portrait model parameters. ij}, where m ij =|f ti -θ j | indicates the deviation between the feature and the parameter;

[0050] S44. Parameter update using an optimized loss function:

[0051]

[0052] in w i The feature weights are dynamically adjusted, and η is the learning rate;

[0053] S45, Based on the updated model parameters Θ t+1 The updated portrait feature vector V is generated through a dynamic portrait correction mechanism. t =g(F t ,Θ t+1 ), and calculate the characteristic rate of change ΔV=|V t -V t-1 |;

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

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

[0056] Optionally, S5 specifically includes:

[0057] S51. Extract the historical feature data set H = {h1, h2, ..., h...} of the expert profile. n} and the corresponding time series T={t1,t2,...,t n}, by modeling nonlinear feature changes, a feature change rate function C(h) is constructed. 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 rate of change function C(h) i ,t i ) and real-time feature set F t ={f1,f2,...,f k Construct a feature mapping model based on dynamic correlation inference to generate the current trend vector V. t =g(C,F t ), where g represents the correlation function;

[0061] S53. Predict future time T by combining a temporal recursive network with the dynamic changes in expert profiles. future =t n+1 ,t n+2 ,...,t n+m} characteristic trends

[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 incremental model based on deep learning;

[0064] S54, Regarding the predicted trend data A multivariate optimization algorithm is used to correct the prediction error ∈:

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

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

[0067] S55, Combining the optimized set of future trend features Generate expert profiles and future trend models M future (T), the model parameters are updated in real time through a feature dynamic fusion algorithm;

[0068] S56, The generated future trend model M future (T) Stored in a distributed storage system and provides a real-time interface for visualization analysis and dynamic decision-making of future characteristics and trends.

[0069] Optionally, S6 specifically includes:

[0070] S61. Construct a distributed storage architecture, including a distributed storage node set N = {n1, n2, ..., n}. m Together with the central coordinating node C0, the consistent hashing algorithm is used to process the portrait data D. t Distribute data to the optimal node according to its characteristics;

[0071] S62. Employ a real-time storage mechanism based on metadata indexing to store expert profile data D. t =d t1 ,d t2 ,...,d tk} By timestamp T={t1,t2,...,t k} and data category labels L={l1,l2,...,l k Generate dynamic multidimensional indexes I t = f(T,L), stored in a distributed set of nodes;

[0072] S63. An adaptive dynamic concurrent update mechanism is adopted, combined with distributed conflict detection and lock management algorithms, to ensure that data can be updated in real time and remain consistent when multiple users are updating. The constraints are as follows:

[0073]

[0074] Where W(n) i ) and R(n i ) represent the sets of write operations and read operations, respectively, and t represents the operation latency;

[0075] S64. Employ a cross-node retrieval algorithm, decomposing Q into a query plan. g ={Q(n1),Q(n2),...,Q(n)} m Query requests are distributed across storage nodes, and low-latency retrieval is achieved based on a global result aggregation model:

[0076]

[0077] Where R(n) i () represents the query result for a single node;

[0078] S65. An improved node load balancing algorithm is adopted, with the node state matrix S(n i )=[u i ,l i ,a i [This is based on dynamically adjusting storage allocation and access paths, where u] i Indicates 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 profile data update history of each node, through a timestamp chain V = {v1, v2, ..., v...} p Implement data version management and rollback;

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

[0081] Optionally, S7 specifically includes:

[0082] S71. Construct an intelligent recommendation engine architecture, including a dynamic profile input module, a recommendation strategy optimization module, a real-time feedback processing module, and a result push module, with the dynamically updated profile feature set V. opt =v1,v2,...,v n};

[0083] S72. In the recommendation strategy optimization module, a recommendation strategy generation method based on multi-objective optimization is adopted to construct the recommendation function S(x,W,C), and the weight matrix W = [w] is optimized in real time by dynamically adjusting the function. 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 F on the recommendation results through the real-time feedback processing module. R =r1,r2,...,r k This includes click behavior, dwell time, selection rate, and user ratings, with a feedback matrix updated in real time.

[0087] S74. During the optimization of the recommendation strategy, the feedback data F is incorporated. R and dynamic portrait features V opt Optimize the recommendation weights W using a deep reinforcement learning model:

[0088]

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

[0090] S75. In the result push module, the final recommendation scheme set P is generated by combining the optimized recommendation function S'(x,W',C). final ={p1,p2,...,p l}, and push the data to the user decision-making system in real time based on the user profile;

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

[0092] The beneficial effects of this invention are:

[0093] (1) By combining multi-source heterogeneous data fusion technology, adaptive feature optimization algorithm and online learning model, this invention provides in-depth analysis and dynamic adaptation capabilities of expert profile features, enabling the system to capture changes in expert features in real time, overcoming the shortcomings of traditional static profile models that are difficult to reflect dynamic states, thereby significantly improving the timeliness and accuracy of the profile, especially in rapidly changing application scenarios.

[0094] (2) This invention achieves efficient real-time storage and low-latency retrieval of profile 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 system's data access latency but also improves the efficiency and reliability of processing dynamically updated data.

[0095] (3) This invention combines an intelligent recommendation engine with a deep learning model to generate accurate recommendation schemes using dynamically updated user profile data, and continuously optimizes the recommendation strategy based on real-time feedback. This method not only meets diverse user needs but also significantly improves the accuracy of recommendation results and the adaptability of the system, providing an efficient and reliable solution for decision support systems. Attached Figure Description

[0096] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0097] Figure 1 This is a general framework diagram of a dynamic updating method for expert profile characterization proposed in this invention;

[0098] Figure 2 This is a data processing flowchart for a dynamic update method for expert profile characterization proposed in this invention. Detailed Implementation

[0099] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0100] refer to Figure 1-2 A dynamic update method for expert profile characterization includes the following steps:

[0101] S1. Construct a multi-source heterogeneous data acquisition module to acquire dynamic behavioral data, knowledge graph association data, and interactive feedback data of experts in real time through distributed acquisition nodes;

[0102] In this embodiment, S1 specifically includes:

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

[0104] S12. In the dynamic behavior perception unit, a high-frequency data capture algorithm is used to monitor the expert's activity trajectory in real time, and the behavior feature vector is represented as 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 parsing unit, based on the multi-hop path reasoning mechanism of the knowledge graph, a semantic network representation of experts and related entities is dynamically generated, with the weight matrix being W = [w ij ], where w ij This indicates the strength of the semantic association between the expert and entity j;

[0106] S14. In the feedback data mining unit, implicit feedback features are extracted from the interaction behavior between users and experts by combining a deep feature learning model, generating an interaction behavior feature set I. f ={i1,i2,...,i p}, where i p The embedding representation of the p-th interaction behavior;

[0107] S15. Apply a distributed data task allocation algorithm to dynamically allocate data acquisition tasks to different acquisition nodes, and adopt a weighted synchronization mechanism T. sync =αT1+βT2, where T1 represents the acquisition delay, T2 represents the processing delay, and α and β are weight parameters;

[0108] S16. Construct a data integrity verification process. Through a two-way verification mechanism between edge nodes and the central storage platform, ensure that dynamic behavioral data, semantically related data, and feedback data remain consistent during uploading and storage. Generate a high-dimensional data representation space through the fusion and dynamic optimization of multi-source heterogeneous data.

[0109] S2. Through a multi-layer data fusion strategy, the collected heterogeneous data is embedded and combined with a context-aware model to achieve dynamic semantic feature extraction.

[0110] In this embodiment, S2 specifically includes:

[0111] S21. Construct 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 realize standardized preprocessing of data features through the heterogeneous parsing module.

[0112] S22. Use a feature embedding generation algorithm to perform high-dimensional feature mapping on structured data, generating a set of feature vectors F. s ={f s1 ,f s2 ,...,f sn};

[0113] S23. For semi-structured data, a hierarchical recursive nested model is adopted to generate a dynamic embedded representation E through multi-level feature association analysis. ss ={e ss1 ,e ss2 ,...,e ssp}, and use a feature filtering algorithm to filter redundant features;

[0114] S24. For unstructured data, a semantic feature extraction method based on multimodal deep learning is adopted, and a feature vector E is generated through a vision-speech-text joint embedding model. us ={e us1 ,e us2 ,...,e usq This enables the fusion of cross-modal features.

[0115] S25. Employ a fusion function based on adaptive weight allocation to integrate the structured data features F. s Semi-structured embedding vector E ss Unstructured embedding vector E usThe fusion is represented as a unified feature representation H(x) = α1F s +α2E ss +α3E us α1, α2, and α3 are optimized and adjusted through a 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 ={v1,v2,...,v k}, and adjust the dimension k after dimensionality reduction according to the data distribution characteristics;

[0117] S27. Transfer the semantic feature vector V d The input dynamic semantic parsing module, combined with the real-time contextual semantic reasoning model, generates the embedded feature set S of the expert profile. c =φ(V d C t ), where C t Given the current semantic context, φ represents the semantic inference function;

[0118] S28. Store the generated embedded feature set in the portrait feature database to provide accurate input for dynamically updating the model, while simultaneously retrieving and optimizing features in real time.

[0119] S3. Adaptive feature selection algorithm is adopted to allocate weight parameters according to real-time dynamic characteristics and accurately optimize the feature distribution of the portrait.

[0120] In this embodiment, S3 specifically includes:

[0121] S31. For the fused feature dataset X = {x1, x2, ..., x...} n A multi-dimensional analysis is performed, and a feature evaluation index set M = {m1, m2, ..., m} is constructed using a real-time dynamic feature extraction model. k}, where m i Indicates feature x i The dynamic correlation, timeliness, and predictive value of [the data / information].

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

[0123]

[0124] Where R(x) i ) represents feature x i Real-time correlation, T(x) i ) represents the time sensitivity of the feature, and λ1 and λ2 are dynamic adjustment coefficients;

[0125] S33, Based on weighting factor w i Select the top k features by importance to generate a preliminary optimized feature set X. opt ={x i |w i >θ,i∈[1,n]}, where θ is a dynamic threshold that is updated in real time based on the feature distribution characteristics;

[0126] S34. Apply the multidimensional feature interaction model to calculate the feature mutual information value I(x) in the preliminary optimized feature set. i ,x j Construct the feature interaction matrix M inter A simplified feature set X is generated by a feature combination optimization strategy that maximizes mutual information gain. refined ;

[0127] S35. Employ a reinforcement learning-based dynamic optimization algorithm 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 feature x i In context C t The contribution value below;

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

[0131] S4. Construct a dynamically updated model based on online learning, iteratively optimize model parameters through incremental training algorithms, and correct feature changes in expert profiles in real time.

[0132] In this embodiment, S4 specifically includes:

[0133] S41. Construct a dynamically updated model based on online learning, defining the parameter set Θ = θ1, θ2, ..., θ n} and the portrait feature mapping function g(F t ,Θ), initialize parameters to generate initial portrait model M0(Θ);

[0134] S42. Design a real-time update algorithm based on incremental learning to update real-time streaming data D. t =dt1 ,d t2 ,...,d tm The model is dynamically updated by inputting data in batches, and a feature subset F is generated using a batch incremental strategy. t ={f t1 ,f t2 ,...,f tk};

[0135] S43, Based on real-time feature subset F t Calculate the difference matrix ΔM = {m} with the current portrait model parameters. ij}, where m ij =|f ti -θ j | indicates the deviation between the feature and the parameter;

[0136] S44. Parameter update using an optimized loss function:

[0137]

[0138] in w i The feature weights are dynamically adjusted, and η is the learning rate;

[0139] S45, Based on the updated model parameters Θ t+1 The updated portrait feature vector V is generated through a dynamic portrait correction mechanism. t =g(F t ,Θ t+1 ), and calculate the characteristic rate of change ΔV=|V t -V t-1 |;

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

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

[0142] S5. Apply the predictive inference mechanism, combine historical feature change rates and current data to generate a future trend model of expert profiles;

[0143] In this embodiment, S5 specifically includes:

[0144] S51. Extract the historical feature data set H = {h1, h2, ..., h...} of the expert profile. n} and the corresponding time series T={t1,t2,...,t n}, by modeling nonlinear feature changes, a feature change rate function C(h) is constructed. 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 rate of change function C(h) i ,t i ) and real-time feature set F t ={f1,f2,...,f k Construct a feature mapping model based on dynamic correlation inference to generate the current trend vector V. t =g(C,F t ), where g represents the correlation function;

[0148] S53. Predict future time T by combining a temporal recursive network with the dynamic changes in expert profiles. future ={t n+1 ,t n+2 ,...,t n+m} characteristic trends

[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 incremental model based on deep learning;

[0151] S54, Regarding the predicted trend data A multivariate optimization algorithm is used to correct the prediction error ∈:

[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, Combining the optimized set of future trend features Generate expert profiles and future trend models M future (T), the model parameters are updated in real time through a feature dynamic fusion algorithm;

[0155] S56, The generated future trend model M future (T) Stored in a distributed storage system and provides a real-time interface for visualization analysis and dynamic decision-making of future characteristics and trends.

[0156] S6. Real-time storage and efficient access to profile data, dynamic concurrent update and retrieval functions are achieved through a distributed storage architecture;

[0157] In this embodiment, S6 specifically includes:

[0158] S61. Construct a distributed storage architecture, including a distributed storage node set N = {n1, n2, ..., n}. m Together with the central coordinating node C0, the consistent hashing algorithm is used to process the portrait data D. t Distribute data to the optimal node according to its characteristics;

[0159] S62. Employ a real-time storage mechanism based on metadata indexing to store expert profile data D. t =d t1 ,d t2 ,...,d tk} By timestamp T={t1,t2,...,t k} and data category labels L={l1,l2,...,l k Generate dynamic multidimensional indexes I t = f(T,L), stored in a distributed set of nodes;

[0160] S63. An adaptive dynamic concurrent update mechanism is adopted, combined with distributed conflict detection and lock management algorithms, to ensure that data can be updated in real time and remain consistent when multiple users are updating. The constraints are as follows:

[0161]

[0162] Where W(n) i ) and R(n i ) represent the sets of write operations and read operations, respectively, and t represents the operation latency;

[0163] S64. Employ a cross-node retrieval algorithm, decomposing Q into a query plan. g ={Q(n1),Q(n2),...,Q(n)} mQuery requests are distributed across storage nodes, and low-latency retrieval is achieved based on a global result aggregation model:

[0164]

[0165] Where R(n) i () represents the query result for a single node;

[0166] S65. An improved node load balancing algorithm is adopted, with the node state matrix S(n i )=[u i ,l i ,a i [This is based on dynamically adjusting storage allocation and access paths, where u] i Indicates 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 profile data update history of each node, through a timestamp chain V = {v1, v2, ..., v...} p Implement data version management and rollback;

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

[0169] S7 integrates an intelligent recommendation engine that generates targeted recommendation solutions based on dynamically updated user profiles and provides real-time feedback to the user's decision-making system.

[0170] In this embodiment, S7 specifically includes:

[0171] S71. Construct an intelligent recommendation engine architecture, including a dynamic profile input module, a recommendation strategy optimization module, a real-time feedback processing module, and a result push module, with the dynamically updated profile feature set V. opt =v1,v2,...,v n};

[0172] S72. In the recommendation strategy optimization module, a recommendation strategy generation method based on multi-objective optimization is adopted to construct the recommendation function S(x,W,C), and the weight matrix W = [w] is optimized in real time by dynamically adjusting the function. 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 F on the recommendation results through the real-time feedback processing module. R =r1,r2,...,r k This includes click behavior, dwell time, selection rate, and user ratings, with a feedback matrix updated in real time.

[0176] S74. During the optimization of the recommendation strategy, the feedback data F is incorporated. R and dynamic portrait features V opt Optimize the recommendation weights W using a deep reinforcement learning model:

[0177]

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

[0179] S75. In the result push module, the final recommendation scheme set P is generated by combining the optimized recommendation function S'(x,W',C). final ={p1,p2,...,p l}, and push the data to the user decision-making system in real time based on the user profile;

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

[0181] Example 1:

[0182] To verify the feasibility of this invention, it was applied to the expert database system of an educational institution B. Institution B possesses a large amount of expert data covering academic, technical, and management fields. However, due to the static updating method of expert profiles, its system's performance in intelligent recommendation and dynamic analysis is unsatisfactory, particularly the accuracy of recommendation schemes and the timeliness of expert characteristics are severely affected. To solve these problems, Institution B decided to deploy a dynamic updating method for expert profile characterization, as described in this invention.

[0183] After applying this invention, the system first collects expert behavior data and interaction records in real time from academic platforms, technical communities, and internal management systems through a multi-source heterogeneous data acquisition module. This data, after cleaning, denoising, and standardization, is input into a data fusion module, where a dynamic weight allocation algorithm is used to optimize the expert profile features, generating high-dimensional feature vectors. The system employs a dynamically updated model based on online learning, updating model parameters in real time through incremental training to capture the dynamic trends of expert profile changes. After the profile update is completed, the intelligent recommendation engine combines the updated profile data to generate personalized recommendation schemes, and achieves efficient data storage and retrieval through a distributed storage architecture.

[0184] In a specific application scenario, the system ran for a month to support Institution B's academic resource allocation decisions. Through dynamically updated expert profiles, the intelligent recommendation engine could recommend the most suitable experts based on the current project needs, while simultaneously capturing real-time changes in experts' research directions, project participation, and collaborative networks. For example, when assembling a research team, the system recommended an expert whose recent research focus shifted to a field relevant to the project; this recommendation significantly improved the project team's success rate.

[0185] To evaluate the effectiveness of this invention, Organization B compared the performance of the expert profiling system before and after deployment. Specific data are shown in Table 1.

[0186] Table 1B Performance Comparison Data of Institutional Expert Profiling Systems

[0187]

[0188]

[0189] As shown in Table 1, the dynamic update method of this invention significantly improves the performance of the expert profiling system. For example, profiling updates are increased from once a month to real-time, recommendation accuracy increases from 67.4% to 92.8%, and data processing latency and recommendation response time are reduced from 45 minutes and 60 minutes to 5 minutes and 10 minutes, respectively. Furthermore, the system's data storage efficiency is improved fourfold, and data redundancy is reduced to 3%. In practical decision support, the rate of obtaining effective support through the profiling system increases from 65.7% to 94.5%, significantly enhancing the system's value in practical applications.

[0190] In one specific scenario, an academic resource allocation decision required matching an expert specializing in "artificial intelligence and ethics research." The system captured a recent high-frequency activity of an expert in this field through dynamic profiling and, based on the weighting mechanism of an intelligent recommendation engine, recommended the expert to the decision-making team. Ultimately, this recommendation was adopted and proved highly effective, significantly enhancing the project's academic impact.

[0191] This invention achieves efficient dynamic updating of expert profiles and a significant improvement in application effectiveness by dynamically capturing changes in expert features, updating the profile model in real time, and using an intelligent recommendation engine for precise matching. It not only solves the problems of lagging updates, low recommendation accuracy, and poor storage efficiency in traditional profile technology, but also provides reliable decision support for Institution B, improving the efficiency and intelligence level of academic resource allocation.

[0192] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A dynamic updating method for expert profile characterization, characterized in that, Includes the following steps: S1. Construct a multi-source heterogeneous data acquisition module to acquire dynamic behavioral data, knowledge graph association data, and interactive feedback data of experts in real time through distributed acquisition nodes; S2. Through a multi-layer data fusion strategy, the collected heterogeneous data is embedded and combined with a context-aware model to achieve dynamic semantic feature extraction. S2 specifically includes: An adaptive feature selection algorithm is adopted to allocate weight parameters based on real-time dynamic characteristics, thereby accurately optimizing the feature distribution of the profile. The fused feature dataset is analyzed from multiple dimensions, and a set of feature evaluation indicators is constructed using a real-time dynamic feature extraction model. Design a dynamic weight allocation function and calculate the feature weight factors through a multi-objective optimization method; The importance of each factor is ranked based on its weight. Based on the characteristics of the features, a preliminary optimized feature set is generated and updated in real time according to the feature distribution characteristics; By applying a multidimensional feature interaction model, the mutual information values ​​of features in the preliminary optimized feature set are calculated, the interaction matrix between features is constructed, and a simplified feature set is generated by a feature combination optimization strategy that maximizes mutual information gain. A dynamic optimization algorithm based on reinforcement learning is used to iteratively update the feature weight factors; Generate the final optimized feature set and input it into the expert profile dynamic update model to complete the precise optimization of feature distribution and real-time update. S3. Adaptive feature selection algorithm is adopted to allocate weight parameters according to real-time dynamic characteristics and accurately optimize the feature distribution of the portrait. S4. Construct a dynamically updated model based on online learning, iteratively optimize model parameters through incremental training algorithms, and correct feature changes in expert profiles in real time. S5. Apply the predictive inference mechanism, combine historical feature change rates and current data to generate a future trend model of expert profiles; S5 specifically includes: Predict future time by combining temporal recursive networks with the dynamic changes in expert profiles; For the predicted trend data, a multivariate optimization algorithm is used to correct the prediction error; S6. Real-time storage and efficient access to profile data, dynamic concurrent update and retrieval functions are achieved through a distributed storage architecture; S6 specifically includes: Construct a distributed storage architecture, including a consistent hashing algorithm to distribute profile data to the optimal nodes according to data characteristics; A real-time storage mechanism based on metadata indexing is adopted to generate dynamic multidimensional indexes for expert profile data according to timestamps and data category tags, and store them in a distributed node set; A distributed multi-version control mechanism is used to record the profile data update history of each node, and data version management and rollback are achieved through a timestamp chain; S7 integrates an intelligent recommendation engine, which generates targeted recommendation solutions based on dynamically updated user profiles and provides real-time feedback to the user's decision-making system. S7 specifically includes: In the recommendation strategy optimization module, a recommendation strategy generation method based on multi-objective optimization is adopted to construct a recommendation function, and the weight matrix is ​​optimized in real time by dynamically adjusting the function. In the process of optimizing the recommendation strategy, a deep reinforcement learning model is used to optimize the recommendation weights by combining feedback data and dynamic profile features.

2. The dynamic updating method for expert profile characterization according to claim 1, characterized in that, S1 specifically includes: S11. Construct a multi-source heterogeneous data acquisition module, including a dynamic behavior perception unit, a semantic association parsing unit, and a feedback data mining unit, and realize real-time data acquisition and intelligent preprocessing through a distributed node cluster based on edge computing. S12. In the dynamic behavior perception unit, a high-frequency data capture algorithm is used to monitor the expert's activity trajectory in real time, and the behavior feature vector is represented as follows: ,in It is a time series. As a behavior category, For contextual semantic features; S13. In the semantic association parsing unit, based on the multi-hop path reasoning mechanism of the knowledge graph, a semantic network representation of experts and related entities is dynamically generated, with the weight matrix as follows: ,in Representing experts and entities The strength of semantic association between them; S14. In the feedback data mining unit, implicit feedback features are extracted from the interaction behavior between users and experts by combining a deep feature learning model, and an interaction behavior feature set is generated. ,in Indicates the first An embedded representation of an interactive behavior; S15. Apply a distributed data task allocation algorithm to dynamically allocate data acquisition tasks to different acquisition nodes, and adopt a weighted synchronization mechanism. ,in Indicates a data acquisition delay. This indicates a processing delay. , These are weight parameters; S16. Construct a data integrity verification process. Through a two-way verification mechanism between edge nodes and the central storage platform, ensure that dynamic behavioral data, semantically related data, and feedback data remain consistent during uploading and storage. Generate a high-dimensional data representation space through the fusion and dynamic optimization of multi-source heterogeneous data.

3. The dynamic updating method for expert profile characterization according to claim 1, characterized in that, S2 specifically includes: S21. Construct 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 realize standardized preprocessing of data features through the heterogeneous parsing module. S22. Use a feature embedding generation algorithm to perform high-dimensional feature mapping on structured data and generate a set of feature vectors. ; S23. For semi-structured data, a hierarchical recursive nested model is adopted to generate dynamic embedded representations through multi-level feature association analysis. And a feature filtering algorithm is used to filter redundant features; S24. For unstructured data, a semantic feature extraction method based on multimodal deep learning is adopted, and feature vectors are generated through a vision-speech-text joint embedding model. This enables the fusion of cross-modal features; S25. Employ a fusion function based on adaptive weight allocation to integrate structured data features. Semi-structured embedding vectors Unstructured embedding vectors Merging into a unified feature representation ,in , , Optimize and adjust through a dynamic weight allocation mechanism; S26. Apply a context-aware dynamic dimensionality reduction algorithm to the fusion result to generate multi-dimensional semantic feature vectors. And adjust the dimensionality after dimensionality reduction according to the data distribution characteristics. ; S27. Transfer semantic feature vectors Input the dynamic semantic parsing module and combine it with the real-time contextual semantic reasoning model to generate the embedded feature set of the expert profile. ,in For the current semantic context, Represents a semantic reasoning function; S28. Store the generated embedded feature set in the portrait feature database to provide accurate input for dynamically updating the model, while simultaneously retrieving and optimizing features in real time.

4. The dynamic updating method for expert profile characterization according to claim 1, characterized in that, S3 specifically includes: S31. The fused feature dataset Multi-dimensional analysis is conducted, and a set of feature evaluation indicators is constructed using a real-time dynamic feature extraction model. ,in This indicates the dynamic correlation, timeliness, and predictive value of the features; S32. Design a dynamic weight allocation function and calculate the feature weight factors using a multi-objective optimization method. : ； in Representation of features Real-time correlation, Indicates the time sensitivity of features. , For dynamic adjustment coefficients; S33, Based on weighting factors Select the most important Based on the characteristics, a preliminary optimized feature set is generated. ,in The threshold is dynamic and is updated in real time based on the characteristics of the feature distribution. S34. Apply the multidimensional feature interaction model to calculate the feature mutual information values ​​in the preliminary optimized feature set. Construct the interaction matrix between features A simplified feature set is generated by a feature combination optimization strategy that maximizes mutual information gain. ; S35. Employ a reinforcement learning-based dynamic optimization algorithm to iteratively update the feature weight factors. : ； in For learning rate, Representation of features In context The contribution value below; S36. Generate the final optimized feature set. The data is then input into the expert profile dynamic update model to achieve precise optimization of feature distribution and real-time updates.

5. The dynamic updating method for expert profile characterization according to claim 1, characterized in that, S4 specifically includes: S41. Construct a dynamically updated model based on online learning and define the parameter set. and portrait feature mapping function Initialize parameters to generate an initial portrait model. ; S42. Design a real-time update algorithm based on incremental learning to update real-time streaming data. The model is dynamically updated by inputting data in batches, and a batch incremental strategy is used to generate feature subsets. ; S43, Based on real-time feature subsets Calculate the difference matrix between the current portrait model parameters and the current portrait model parameters. ,in Indicates the deviation between the feature and the parameter; S44. Parameter update using an optimized loss function: ； in , For dynamically adjusted feature weights, The learning rate; S45, Based on the updated model parameters The updated image feature vector is generated through a dynamic image correction mechanism. And calculate the characteristic rate of change. ; S46. Introduce dynamic feature filtering and priority adjustment algorithms for features with a change rate exceeding a threshold. The features are weighted and amplified to generate an optimized feature representation. ; S47. Utilize the updated portrait model The system enables dynamic correction of expert profiles and stores the optimization results using a distributed storage architecture.

6. The dynamic updating method for expert profile characterization according to claim 1, characterized in that, S5 specifically includes: S51. Extracting historical feature data sets of expert profiles and the corresponding time series By modeling nonlinear feature changes, a feature change rate function is constructed. : ； in This indicates the nonlinear change of the feature over time. Indicates a point in time; S52. Using the characteristic rate of change function With real-time feature set Construct a feature mapping model based on dynamic correlation inference to generate a current trend vector. ,in Indicates an association function; S53. Predict future time by combining temporal recursive networks with the dynamic changes in expert profiles. Characteristic trends : ； in This represents a time-dynamic incremental model based on deep learning; S54, Regarding the predicted trend data A multivariate optimization algorithm is used to correct the prediction error. : ； in The error correction factor is jointly determined by historical data and current dynamic characteristics; S55, Combining the optimized set of future trend features Generate expert profiles and future trend models The model parameters are updated in real time through a feature dynamic fusion algorithm; S56, The generated future trend model It stores data in a distributed storage system and provides a real-time interface for visualization analysis and dynamic decision-making based on future characteristics and trends.

7. The dynamic updating method for expert profile characterization according to claim 1, characterized in that, S6 specifically includes: S61. Construct a distributed storage architecture, including a set of distributed storage nodes. and central coordination node The consistent hashing algorithm is used to process the portrait data. Distribute data to the optimal node according to its characteristics; S62. Employ a real-time storage mechanism based on metadata indexing to store expert profile data. By timestamp and data category labels Generate dynamic multidimensional indexes It is stored in a distributed set of nodes; S63. An adaptive dynamic concurrent update mechanism is adopted, combined with distributed conflict detection and lock management algorithms, to ensure that data can be updated in real time and remain consistent when multiple users are updating. The constraints are as follows: ； in and These represent the sets of write operations and read operations, respectively. Indicates operation delay; S64. Employ a cross-node retrieval algorithm, through query plan decomposition. Query requests are distributed across storage nodes, and low-latency retrieval is achieved based on a global result aggregation model: ； in This is a single-node query result; S65. An improved node load balancing algorithm is adopted, using the node state matrix. To dynamically adjust storage allocation and access paths, where Indicates node utilization. Indicates the current load. Indicates available resources; S66. Use a distributed multi-version control mechanism to record the profile data update history of each node through a timestamp chain. Implement data version management and rollback; S67. Integrating the distributed storage architecture with the dynamic update module enables efficient real-time storage of profile data, concurrent updates across nodes, and low-latency retrieval.

8. The dynamic updating method for expert profile characterization according to claim 1, characterized in that, Specifically, S7 includes: S71. Construct an intelligent recommendation engine architecture, including a dynamic profile input module, a recommendation strategy optimization module, a real-time feedback processing module, and a result push module, with dynamically updated profile feature sets. ; S72. In the recommendation strategy optimization module, a recommendation strategy generation method based on multi-objective optimization is adopted to construct the recommendation function. The weight matrix is ​​optimized in real time by dynamically adjusting the function. : ； in For the candidate recommendation set, For context variables, This represents a personalized score indicating the recommendation results. and For dynamic adjustment coefficients; S73. Collect user interaction data on recommendation results through the real-time feedback processing module. This includes click behavior, dwell time, selection rate, and user ratings, with a feedback matrix updated in real time. S74. In the process of optimizing the recommendation strategy, feedback data is incorporated. and dynamic portrait features Optimize recommendation weights using deep reinforcement learning models : ； in For learning rate, This represents the recommendation loss function, which is dynamically adjusted based on real-time feedback data. S75. In the results push module, combine the optimized recommendation function. Generate the final set of recommended solutions. And push it to the user decision-making system in real time based on the user profile; S76. Introduce a visualization analysis interface into the recommendation engine to display the generation logic of the recommendation strategy and the optimization process based on user feedback, while dynamically adjusting the recommendation parameters.

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