Intelligent science and technology consultation decision support system

Abstract

The invention relates to the technical field of science and technology consultation, and provides an intelligent science and technology consultation decision support system, which comprises an information acquisition module, a data processing module, a knowledge base module, an inference engine module, a user interaction interface, a learning optimization module and a system security module. According to the intelligent scientific and technological consultation decision support system provided by the invention, a plurality of advanced leading-edge technologies such as multi-source information collection, multi-modal data fusion, federated learning, knowledge graph construction, multi-strategy reasoning, augmented reality interaction, meta-learning and block chain security are fully fused; and a set of complete, intelligent, flexible and safe science and technology consultation solution is formed. The system not only realizes breakthrough in the aspects of data processing and knowledge management, but also shows remarkable advantages in the aspects of reasoning ability, user experience, information security and the like, and provides solid technical support for scientific and technological innovation, industrial upgrading and decision-making intellectualization.

Description

Technical Field

[0001] This invention relates to the field of science and technology consulting technology, and more specifically to an intelligent science and technology consulting decision support system. Background Technology

[0002] With the rapid development of science and technology and the continuous advancement of information technology, the scientific research and technical consulting fields face the challenge of massive, multi-source, and heterogeneous data. How to efficiently collect, process, and utilize this data has become crucial for improving the level of scientific and technological decision-making. However, existing scientific and technological consulting systems often rely on single channels or manual screening for data collection, making it difficult to achieve comprehensive coverage and real-time updates of text, images, voice, and structured data, resulting in information lag and incompleteness. Furthermore, traditional data processing often employs a centralized approach, posing risks of data privacy leaks and failing to meet the needs of cross-institutional and multi-departmental collaborative modeling, limiting the generalization ability and application scope of models. In terms of knowledge management, existing systems often rely on static knowledge bases, lacking dynamic updates and adaptive capabilities, making it difficult to keep up with the rapid evolution of knowledge in the scientific and technological field, affecting the scientific rigor and accuracy of decision-making. Regarding reasoning mechanisms, most systems employ a single reasoning method, making it difficult to consider the diversity and uncertainty of complex problems, resulting in a lack of flexibility and depth in decision-making outcomes. User interaction methods are also relatively simple, lacking immersion and natural language support, failing to meet users' multi-turn, multimodal interaction needs, and impacting user experience and service efficiency. Meanwhile, with increasingly stringent data security and compliance requirements, traditional systems show significant shortcomings in ensuring data security, process transparency, and tamper resistance, making it difficult to meet regulatory and auditing needs. In summary, existing technology consulting decision support systems have considerable room for improvement in data collection, processing, knowledge management, reasoning capabilities, user interaction, and security. There is an urgent need for a new system that integrates multiple advanced technologies to provide efficient, multi-dimensional, dynamic, and secure intelligent consulting services to meet the diverse needs of modern technology management and decision-making.

[0003] Therefore, this solution proposes an intelligent technology consulting and decision support system to address the aforementioned issues. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide an intelligent technology consulting and decision support system.

[0005] To achieve the aforementioned objective, the technical solution of the present invention is implemented as follows: an intelligent science and technology consulting decision support system, comprising:

[0006] The information acquisition module is responsible for the efficient acquisition and analysis of multi-source heterogeneous scientific and technological information;

[0007] The data processing module is used to improve data quality and enable joint modeling of data across institutions;

[0008] The knowledge base module builds and dynamically updates the domain knowledge base;

[0009] The inference engine module enables multi-strategy hybrid inference and dynamic decision generation;

[0010] The user interface supports immersive, multi-turn natural language interaction.

[0011] The learning optimization module adaptively updates the model based on user feedback.

[0012] The system security module ensures the security and compliance of data and decision-making processes.

[0013] Preferably, the information acquisition module is based on multimodal data fusion technology. Through proactive intelligent crawling and semantic filtering algorithms, it can acquire and parse text, images, voice and structured data at high frequency and low latency, and support real-time dynamic updates of scientific and technological information.

[0014] Preferably, the data processing module adopts a federated learning-based optimization strategy, which improves data quality through data preprocessing methods including denoising, completion, and compression, while reducing communication efficiency and computational overhead, thereby achieving efficient joint modeling of cross-institutional data, ensuring data privacy and security, and improving model generalization ability.

[0015] Preferably, the knowledge base module is based on adaptive knowledge graph construction technology, utilizes knowledge completion algorithms and multi-level association mechanisms, and combines domain expert knowledge with automatic extraction technology to achieve continuous evolution and dynamic updates of knowledge, supporting the construction of knowledge bases in specific domains.

[0016] Preferably, the inference engine module integrates symbolic logic reasoning, probabilistic reasoning, and deep reinforcement learning algorithms. Through a multi-strategy hybrid inference framework, it enables multi-angle analysis and dynamic decision generation of complex scientific and technological problems, supporting applications in multiple fields.

[0017] Preferably, the user interface is based on augmented reality (AR) and natural language understanding technology, designed to provide an immersive, multi-turn conversational intelligent consultation service, supporting users' natural language input and interaction, and enhancing visual feedback and user experience through augmented reality technology.

[0018] Preferably, the learning optimization module uses a meta-learning mechanism combined with user behavior analysis and feedback loop to achieve adaptive updating of the model based on few-shot learning, thereby improving the personalization and accuracy of the consultation plan and promoting continuous optimization of system performance.

[0019] Preferably, the system security module utilizes blockchain technology to ensure the traceability and tamper-proof protection of data and decision-making processes, and uses smart contracts to automate the regulatory consultation service process, thereby ensuring the fairness and compliance of the system.

[0020] Preferably, an intelligent technology consulting decision support method for an intelligent technology consulting decision support system includes:

[0021] Utilizing multimodal fusion technology, text, images, speech, and structured data are automatically collected and parsed through proactive intelligent crawling and semantic understanding algorithms;

[0022] By using a federated learning framework for data preprocessing and feature extraction, data quality can be optimized, and model training efficiency and generalization ability can be improved.

[0023] An adaptive knowledge graph is constructed based on graph neural networks, and continuous evolution and dynamic updating of knowledge are achieved through knowledge completion and multi-level association mechanisms.

[0024] A multi-strategy hybrid reasoning framework is adopted, combining symbolic reasoning, probabilistic reasoning and deep reinforcement learning, to generate diverse and dynamically optimized consultation decision-making schemes;

[0025] By leveraging augmented reality and natural language understanding technologies, we can provide immersive, multi-turn conversational intelligent consultation services, supporting users' natural language input and interaction.

[0026] By leveraging meta-learning mechanisms combined with user feedback, adaptive updates of models based on few-shot learning are achieved, thereby improving the system's adaptability.

[0027] By using blockchain technology, the consulting service process can be automated for supervision and result verification, ensuring the fairness and compliance of the system.

[0028] The beneficial effects of this invention are reflected in:

[0029] High efficiency and real-time performance of data acquisition: By adopting a distributed crawler architecture combined with semantic filtering algorithms, it can quickly and accurately capture text, images, voice and structured information from different channels, realize the dynamic updating of scientific and technological information, and provide the latest and most comprehensive data foundation for decision-making.

[0030] Data quality assurance and privacy security: Federated learning technology avoids centralized storage of sensitive data, effectively protecting data privacy and improving the model's generalization ability. Meanwhile, data preprocessing steps such as denoising, completion, and compression help improve the efficiency and accuracy of model training.

[0031] Continuous evolution and dynamic updating of the knowledge base: Based on adaptive knowledge graph technology, combined with knowledge completion and multi-level association mechanisms, new knowledge can be continuously introduced and the relationship network can be improved to ensure the timeliness and accuracy of the knowledge base and provide reliable knowledge support for complex scientific and technological problems.

[0032] Multi-strategy fusion reasoning capability: Integrating symbolic logic reasoning, probabilistic reasoning, and deep reinforcement learning, it achieves multi-angle and multi-level intelligent reasoning, which can select the optimal strategy for different types of problems and improve the diversity and adaptability of decision-making.

[0033] Immersive human-computer interaction experience: Combining augmented reality technology with natural language understanding, it realizes multi-turn, multi-modal human-computer interaction, greatly improving the user experience, enabling users to obtain consultation services in a natural and intuitive way, and enhancing the system's friendliness and usability.

[0034] Enhanced personalization and adaptability: By leveraging meta-learning mechanisms and user behavior analysis, the model can be rapidly adapted and customized to meet the specific needs of different users, thereby improving the relevance and accuracy of consulting solutions.

[0035] System security and compliance assurance: Blockchain technology is used to achieve full-process traceability and data integrity, ensuring the fairness, transparency and trustworthiness of the system, effectively preventing data tampering and improper operation, and complying with relevant laws and industry standards.

[0036] In summary, the intelligent technology consulting and decision support system provided by this invention fully integrates advanced technologies such as multi-source information acquisition, multimodal data fusion, federated learning, knowledge graph construction, multi-strategy reasoning, augmented reality interaction, meta-learning, and blockchain security, forming a complete, intelligent, flexible, and secure technology consulting solution. This system not only achieves breakthroughs in data processing and knowledge management but also demonstrates significant advantages in reasoning capabilities, user experience, and information security, providing solid technical support for technological innovation, industrial upgrading, and intelligent decision-making.

[0037] Its broad application prospects cover multiple fields such as scientific research institutions, corporate innovation departments, and government science and technology management. It helps improve the efficiency and scientific nature of science and technology decision-making, reduce human subjective bias, accelerate the pace of scientific and technological achievements transformation, and promote the deep integration of science and technology with industry. In addition, the system's modular design and flexible expansion capabilities provide a good foundation for the integration of more emerging technologies (such as artificial intelligence, the Internet of Things, and big data analytics) in the future, and have great promotional value and application potential.

[0038] In summary, this invention not only achieves multiple innovations at the technical level, but also demonstrates enormous economic value and social benefits in practical applications. It helps promote the intelligent transformation of science and technology management and innovation systems, and makes an important contribution to achieving the strategy of building a strong nation in science and technology and innovation-driven development. Attached Figure Description

[0039] In the attached diagram:

[0040] Figure 1This is a schematic diagram showing the connection relationship between the various modules of the present invention;

[0041] Figure 2 This is a schematic diagram illustrating the steps of the intelligent technology consulting and decision support method of the present invention. Detailed Implementation

[0042] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only a part of the embodiments of the invention, and not all of them. Unless otherwise specified, the embodiments and features described in this application can be combined with each other. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0043] Please refer to the instruction manual appendix. Figures 1-2 This invention provides an intelligent technology consulting and decision support system:

[0044] Example 1: System Overall Architecture Design

[0045] This system adopts a modular design approach, with each functional module working together to build a comprehensive intelligent consulting service system. For the information collection module, the system employs a distributed crawler architecture, deploying multiple crawler nodes with different functions, each executing different data collection strategies. The crawler system is based on the Scrapy framework and extended with semantic filtering algorithms to effectively eliminate low-quality information. For the fusion of multimodal data, the system uses a combination of early and late fusion methods: text data is semantically encoded using the BERT model, image data uses ResNet to extract features, and speech data is processed using the Wav2Vec model, achieving efficient parsing of multiple data types.

[0046] The data processing module incorporates a federated learning framework, employing the FedAvg algorithm for cross-institutional model training. Each participating institution completes model training locally, uploading only the model parameters, thus avoiding the transmission of raw data. The data preprocessing workflow includes wavelet transform denoising, matrix completion to fill in missing data, and principal component analysis for dimensionality reduction. Regarding communication efficiency, gradient compression and parameter quantization techniques significantly reduce communication overhead, improving overall training efficiency.

[0047] Example 2: Construction and Maintenance of the Knowledge Base Module

[0048] The knowledge base is built using knowledge graph technology and employs knowledge representation learning algorithms such as TransE and RotatE to map entities and their relationships into a vector space. For knowledge completion, Graph Convolutional Networks (GCNs) combined with multi-hop neighbor information are used to predict missing entity relationships. The knowledge association mechanism covers multiple levels: entity level (direct relationships), concept level (hyper-hyper-subordinate relationships), and semantic level (similarity relationships).

[0049] When the knowledge base is dynamically updated, a threshold trigger mechanism is set up. When the confidence level of newly collected information exceeds 0.8, the system automatically starts the incremental learning algorithm to avoid retraining the entire knowledge graph. The knowledge of domain experts is integrated with automatically extracted knowledge through ontology mapping technology to ensure the accuracy and timeliness of the knowledge base.

[0050] Example 3: Multi-strategy fusion of inference engines

[0051] The inference engine integrates three methods: symbolic logic reasoning, probabilistic reasoning, and deep reinforcement learning. Symbolic logic reasoning constructs a first-order predicate logic rule base, combining forward and backward chaining reasoning strategies to support the step-by-step decomposition of complex queries. The probabilistic reasoning part employs a Bayesian network model, using variational inference and belief propagation algorithms to balance inference accuracy and computational efficiency. Deep reinforcement learning designs a state space that includes current question features, historical interactions, and the knowledge base state; an action space that encompasses multiple inference strategy choices; and a reward function designed based on user satisfaction and answer accuracy, using the Actor-Critic algorithm to continuously optimize the strategy.

[0052] Example 4: User Interface Design

[0053] The user interface combines augmented reality (AR) technology with natural language processing, built on the ARCore (Android) and ARKit (iOS) frameworks. A 3D knowledge graph is displayed stereoscopically using the Unity3D engine, allowing users to browse knowledge nodes via gestures and click to view detailed information. Natural language understanding employs a BERT-based intent recognition model with an accuracy exceeding 95%, while entity recognition uses a BiLSTM-CRF model, supporting the identification of domain-specific entities. The dialogue management module maintains multi-turn dialogue contexts to ensure natural and smooth interaction.

[0054] Example 5: Implementation of the Learning Optimization Module

[0055] The learning optimization module introduces a meta-learning mechanism, employing the Model-Agnostic Meta-Learning (MAML) algorithm to enable the model to quickly adapt to new tasks. The system collects user behavioral data such as clicks, dwell time, and feedback ratings, and uses clustering algorithms to categorize users, providing personalized services to different user groups. The feedback loop mechanism is implemented through an online learning algorithm, enabling real-time adjustment of model parameters and continuous improvement of system performance.

[0056] Example 6: System Security Assurance Scheme

[0057] In terms of security, a consortium blockchain architecture is adopted, with participating nodes including consulting service providers, regulatory agencies, and third-party auditing units. The underlying blockchain platform uses Hyperledger Fabric, supporting the deployment and execution of smart contracts. The smart contract design includes consulting service contracts, audit contracts, and data access contracts, responsible for service process recording, quality compliance checks, and sensitive information access control, respectively. The system ensures traceability and data integrity throughout the service process through unique transaction IDs and a Merkle tree structure, effectively preventing tampering.

[0058] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0059] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0060] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. An intelligent technology consulting and decision support system, characterized in that, include: The information acquisition module is responsible for the efficient acquisition and analysis of multi-source heterogeneous scientific and technological information; The data processing module is used to improve data quality and enable joint modeling of data across institutions; The knowledge base module builds and dynamically updates the domain knowledge base; The inference engine module enables multi-strategy hybrid inference and dynamic decision generation; The user interface supports immersive, multi-turn natural language interaction. The learning optimization module adaptively updates the model based on user feedback. The system security module ensures the security and compliance of data and decision-making processes.

2. The intelligent technology consulting and decision support system according to claim 1, characterized in that, The information acquisition module is based on multimodal data fusion technology. Through active intelligent crawling and semantic filtering algorithms, it can acquire and parse text, images, voice and structured data at high frequency and low latency, and supports real-time dynamic updates of scientific and technological information.

3. The intelligent technology consulting and decision support system according to claim 1, characterized in that, The data processing module adopts a federated learning-based optimization strategy. Through data preprocessing methods including denoising, completion, and compression, it improves data quality while reducing communication efficiency and computational overhead. This enables efficient joint modeling of cross-institutional data, ensures data privacy and security, and improves model generalization ability.

4. The intelligent technology consulting and decision support system according to claim 1, characterized in that, The knowledge base module is based on adaptive knowledge graph construction technology. It utilizes knowledge completion algorithms and multi-level association mechanisms, combined with domain expert knowledge and automatic extraction technology, to achieve continuous evolution and dynamic updates of knowledge, and supports the construction of knowledge bases in specific domains.

5. The intelligent technology consulting and decision support system according to claim 1, characterized in that, The inference engine module integrates symbolic logic reasoning, probabilistic reasoning, and deep reinforcement learning algorithms. Through a multi-strategy hybrid inference framework, it enables multi-angle analysis and dynamic decision generation for complex scientific and technological problems, supporting applications in multiple fields.

6. The intelligent technology consulting and decision support system according to claim 1, characterized in that, The user interface is based on augmented reality (AR) and natural language understanding technologies, designed to provide an immersive, multi-turn conversational intelligent consultation service. It supports users’ natural language input and interaction, and enhances visual feedback and user experience through augmented reality technology.

7. The intelligent technology consulting and decision support system according to claim 1, characterized in that, The learning optimization module uses a meta-learning mechanism combined with user behavior analysis and feedback loop to achieve adaptive updates of the model based on few-shot learning, thereby improving the personalization and accuracy of consultation solutions and promoting continuous optimization of system performance.

8. The intelligent technology consulting and decision support system according to claim 1, characterized in that, The system security module utilizes blockchain technology to ensure the traceability and tamper-proof protection of data and decision-making processes, and uses smart contracts to automate the regulatory consultation service process, ensuring the fairness and compliance of the system.

9. An intelligent science and technology consulting decision support method based on the system described in any one of claims 1 to 8, characterized in that, include: Utilizing multimodal fusion technology, text, images, speech, and structured data are automatically collected and parsed through proactive intelligent crawling and semantic understanding algorithms; By using a federated learning framework for data preprocessing and feature extraction, data quality can be optimized, and model training efficiency and generalization ability can be improved. An adaptive knowledge graph is constructed based on graph neural networks, and continuous evolution and dynamic updating of knowledge are achieved through knowledge completion and multi-level association mechanisms. A multi-strategy hybrid reasoning framework is adopted, combining symbolic reasoning, probabilistic reasoning and deep reinforcement learning, to generate diverse and dynamically optimized consultation decision-making schemes; By leveraging augmented reality and natural language understanding technologies, we can provide immersive, multi-turn conversational intelligent consultation services, supporting users' natural language input and interaction. By leveraging meta-learning mechanisms combined with user feedback, adaptive updates of models based on few-shot learning are achieved, thereby improving the system's adaptability. By using blockchain technology, the consulting service process can be automated for supervision and result verification, ensuring the fairness and compliance of the system.

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