1. Project name and abstract

Project Title: Purpose-Driven and Cognitive Feedback Artificial Awareness Service System

Abstract: In view of the current pain points of the lack of understanding and cognitive feedback of users' purpose in the current intelligent Q&A and education system, this project aims to build a set of "purpose-driven and cognitive feedback" artificial awareness service system. The system adopts the original DIKWP cognitive model, which runs through the five-layer semantic architecture of data, information, knowledge, Wisdom and Purpose. Based on the theory of semantic mathematics and the conversion method from conceptual space to semantic space, the core functions such as intelligent question answering, personalized teaching, learning cognitive state recognition and adaptive feedback are realized. The system is modularly deployed under the framework of XaaS cloud services, and can be flexibly reused in open scenarios such as education, customer service, and government affairs. Through the verification of the artificial consciousness white-box evaluation system, the system performs well in semantic modeling, purpose understanding and cognitive decision-making. The project will bring a new technological paradigm to the application of Wisdom education and cognitive intelligence.

2. Project background and problem positioning

With the development of artificial intelligence technology, intelligent Q&A and teaching systems are becoming more and more widely used in various fields. However, there are still obvious shortcomings in the current mainstream schemes: (1) lack of in-depth purpose understanding: traditional dialogue or teaching AI can only respond based on surface text, which is difficult to accurately grasp the real purpose and needs of users, resulting in off-topic answers or teaching content that is not suitable for individuals; (2) Lack of cognitive state feedback: the existing systems are mostly one-way information output, which cannot perceive the cognitive state or emotional response of learners, and cannot be adjusted twice according to the user's understanding, and the level of personalization is limited; (3) Insufficient knowledge integration and reasoning: Many Q&A systems rely on pre-trained large models to generate answers, but lack the support of internal knowledge graphs, which makes it difficult to ensure the accuracy and depth of reasoning in professional fields, and users often obtain one-sided answers. (4) Low explainability and credibility: The existing AI decision-making process is like a black box, and it is difficult for users and developers to understand the basis of the model to give answers, especially in scenarios that require rigorous and transparent requirements such as education and government affairs, which has become a bottleneck in promotion. The above pain points lead to poor user experience and limited teaching effect, which restricts the in-depth application of intelligent services in scenarios such as Wisdom education, intelligent customer service, and government affairs consulting. This project locates the above problems, proposes to introduce the concept of artificial consciousness, and breaks through the existing bottlenecks through the purpose-driven deep semantic modeling and cognitive feedback closed-loop mechanism, so as to provide more intelligent, efficient and credible solutions for related industries.

3. Technical solutions and architecture

The artificial consciousness service system constructed in this project takes the DIKWP cognitive model as the core framework, and divides the user interaction process into five semantic levels: data, information, knowledge, Wisdom, and Purpose, forming a complete link from perception to decision-making. The overall architecture of the system is as follows:

Purpose recognition and user portrait: When a user asks a question or needs to learn, the system first uses natural language processing technology to parse the expression and identify the explicit and implicit purpose. Combined with the user's historical data and preferences, the DIKWP model portrait of the user is constructed, and the existing knowledge state and cognitive characteristics (such as the information that has been mastered, the current confusion points, etc.) are extracted. This step ensures that the system understands not only the "what" but also the "why" and "what it is expected to achieve".

Conceptual semantic transformation and semantic mathematical analysis: According to the identified user purpose and input content, the system uses semantic mathematical theory to convert the symbolic concept representation into DIKWP semantic space representation. Through the formal mapping method, the user's question or learning content is mapped to the data, information, knowledge and other typed resources, and the clear context semantics are given. This process ensures that the machine's understanding of the user's language is not limited to the literal symbolic level, but enters the semantic level that conforms to the user's context, so as to eliminate ambiguity and improve the accuracy of understanding. Semantic mathematics provides a rigorous system of semantic axioms, which makes concept transformation and reasoning evidence-based, and ensures semantic consistency and interpretability.

Knowledge Graph Reasoning and Wisdom Decision-making: After the semantic representation is completed, the system uses the data-information-knowledge graph three-way architecture to retrieve, associate and reason the content. Firstly, the entities and attributes involved in the original problem are matched on the data graph, and the basic data and its frequency characteristics are obtained. Then, the relationship between entities is tracked along the information graph, and relationship metrics such as interaction frequency are calculated to integrate relevant information. Then apply rules and background knowledge on the knowledge graph to make abstract inferences about the information to form a deeper understanding of the problem and possible answers. For example, for questions in educational scenarios, the data layer extracts the surface information, the information layer associates relevant knowledge points or prerequisite knowledge, and the knowledge layer combines the syllabus and cognitive rules to reason and complete the answers. On this basis, the system introduces the Wisdom**(W) layer for decision-making optimization, that is, comprehensively evaluates a variety of candidate answers or teaching strategies, and selects the scheme that best suits the user's purpose and cognitive level under the guidance of expert experience or strategy model. In the whole inference process, the user's Purpose** (P-layer) is also referred to to guide the knowledge search and selection to ensure that the output is consistent with the user's goal. This multi-level processing from data to Wisdom to Purpose realizes a cognitive computing framework that combines bottom-up perception and top-down adjustment: high-level Purpose and Wisdom can reverse guide the processing weight of underlying data and information to form a closed-loop optimization.

Response generation and interactive presentation: After inference decision-making, the system generates corresponding natural language answers or teaching content and pushes it to users. Answer generation is not only based on the content of the knowledge graph, but also integrates large-scale pre-trained language models (such as the DeepSeek series developed by the team) to ensure the fluency and richness of language expression. During the generation process, the system appends semantic annotations and source justification (e.g., citing knowledge base entries) to the answers to enhance the interpretability and credibility of the results. For teaching scenarios, the system can adjust the difficulty and style of the explanation in real time to make it both in line with the user's knowledge background and inspiring. Due to the introduction of emotional and cognitive features in user portraits, the system can also choose appropriate wording and interaction strategies for the user's possible emotional states (such as confusion and frustration), such as adding reassuring and encouraging sentences to improve the interaction experience.

Cognitive feedback and adaptive optimization: After the user receives the response, the system does not stop working, but continuously improves the service effect through the cognitive feedback mechanism. Specifically, the system captures the user's response to the interaction through a variety of means: such as analyzing follow-up questions or feedback questions to determine whether the user is satisfied with the answer, or evaluating the user's mastery of knowledge points through quizzes in teaching. If users are found to still have misunderstandings or knowledge blind spots, the system will trigger a new round of targeted coaching or explanations to achieve round-by-round refinement of human-machine dialogue. This adaptive feedback process benefits from the closed-loop design of the DIKWP model, where high-level Purpose Recognition is combined with Low-level data analysis: the system injects new feedback data into the user's DIKWP user model again, updates its knowledge and Purpose state, and makes targeted adjustments in the next interaction. For example, if it is detected that the user still does not understand a certain concept in teaching, the system will adjust the teaching strategy at the Wisdom layer (slow down the pace or change the teaching method), and reposition the user's goal at the Purpose layer (whether it needs to be a basic make-up lesson or give examples), so as to dynamically optimize the teaching plan. Through continuous cognitive feedback, the system can simulate the process of teaching teachers according to their aptitude, so that users can receive gradually optimized services every time they ask questions or learn.

XaaS modular deployment: The system adopts the cloud architecture design of "Everything as a Service", and each functional module (Purpose parsing, semantic transformation, knowledge graph reasoning, content generation, feedback analysis, etc.) provides standard interfaces in the form of microservices, which can be flexibly expanded and deployed on demand. Different application scenarios can selectively reuse the combination of related modules: for example, the education scenario focuses on the teaching strategy module and learner profiling, while the customer service scenario focuses on the domain knowledge base and sentiment analysis module. The whole system is easy to integrate into various business platforms through containerization and orchestration, and supports multiple deployment methods such as private cloud and public cloud, which is convenient for subsequent commercial promotion and large-scale application.

In the above structure, each key technical link is supported by the team's years of research and development achievements. The DIKWP model and three-point graph architecture have been proposed in a number of patents. The conceptual space to semantic space conversion method has been studied and verified, which can effectively eliminate the difference in agent semantic understanding. Purpose-driven content generation improves the relevance and efficiency of human-machine communication; The closed-loop mechanism of cognitive feedback reflects the self-adjustment ability of the artificial consciousness system. The organic combination of these technologies constitutes a complete scheme of the artificial consciousness service system of this project.

4. Innovativeness and theoretical breakthroughs

Based on the original research of Professor Yucong Duan's team, this project has achieved a number of innovative breakthroughs in semantic modeling, cognitive assessment, and purpose control.

DIKWP cognitive model: Innovatively extends the classic DIKW (pyramid) model and introduces the Purpose layer on top of the data, information, knowledge, and Wisdom layers. This extension transforms cognitive processing from a reactive, data-driven to a proactive, goal-oriented process. The DIKWP model emphasizes the networked interaction and two-way regulation between various levels, and the high-level Purpose and Wisdom can guide the interpretation and screening of underlying data and information to form a closed-loop cognitive process. Compared with the traditional linear model, DIKWP is closer to the characteristics of human cognition, realizes the representation of "purposefulness" by the AI system, and lays the theoretical foundation for the framework of artificial consciousness.

Semantic Mathematics and Formal Semantic Representation: The project adopts the semantic mathematics theory independently developed by the team to introduce semantic explicit into the mathematical formal system. By constructing semantic axioms (such as existence, uniqueness, transitivity, etc.) and formal rules, the meaning carried by symbols is axiomatized. This breakthrough makes knowledge representation and reasoning not only stay at the level of symbolic operation, but also contain clear semantic constraints, realizing the synchronization of logical reasoning and semantic understanding. In similar studies, semantic factors are often implicit in artificially set ontologies or rules, while the semantic mathematical system makes each data and each theorem correspond to a clear semantic explanation. This provides a highly interpretable, rigorous and unified modeling method for knowledge graph and natural language understanding, and solves the long-standing problem of disconnection between formal logic and semantic expression.

Artificial Awareness White-box Assessment System: The team originally developed a white-box DIKWP assessment method to quantitatively evaluate the "awareness" and cognitive ability of large models. Compared with the traditional test that focuses on Q&A performance, this evaluation system constructs a full-link index system covering four modules: perception and information processing, knowledge construction and reasoning, Wisdom application and problem solving, and purpose identification and adjustment. Through hundreds of customized test questions, the mainstream large language model is systematically analyzed, which can break through the traditional mode of focusing only on semantic understanding and reasoning, and analyze the cognition and decision-making process of the model in an all-round way。 This work has created a new dimension of AI cognitive assessment, and it has also fed back the development of the system of this project, which we have borrowed from the white-box evaluation indicators and integrated them into the system design, so that the processing process at all layers of the model can be transparent and monitorable. For example, for the Purpose Recognition module, we have developed an internal assessment similar to "Purpose Understanding Accuracy"; For the Wisdom decision-making module, evaluation such as "solution rationality" is introduced. The application of the white-box evaluation system ensures that the system can continuously optimize each cognitive module during the development process, and create an "explainable and measurable" artificial consciousness system benchmark.

Purpose-driven semantic communication and control: This project introduces a purpose-driven mechanism into AI human-computer interaction to realize the innovation of the system to dynamically adjust the communication content and decision-making strategy according to the multi-party purpose. Specifically, we adopt the theory of conceptual space → semantic space transformation to support the individualized semantic alignment between agents according to their respective DIKWP contexts. Different from traditional communication methods that rely on shared static ontologies, our approach semantically augments and transforms the sender's content according to the receiver's DIKWP model, so as to customize the message that the receiver can easily understand and conform to its cognitive background. This kind of purpose perception and content adaptation significantly reduces misunderstandings in human-computer communication. For example, in the customer service scenario, the system will balance the purpose of the user and the service provider: it not only meets the needs of the user's problem, but also takes into account the service specifications of the enterprise, so as to achieve the dynamic balance of the three-party purpose, so as to provide interactive results that are more in line with the user's needs at a lower cost. For example, CN113810480A proposed an emotional communication method based on DIKW content objects, which typed the sent content and converted it according to the receiver's graph, so that the sender's purpose could be more appropriately expressed and the understanding error would be reduced. Drawing on this idea, this system enables AI to perceive and respond to users' implied purpose and emotions in real time, and truly realize user-centered intelligent services.

In summary, this project theoretically integrates cutting-edge achievements in the fields of cognitive science, semantic web, and artificial intelligence, and forms a unique technical system: semantic modeling for intrinsic purpose, interpretable cognitive evaluation criteria, and dynamic purpose-driven control. This is the first of its kind at home and abroad, and it is expected to drive the paradigm shift of AI from a "black box tool" to a "conscious service body".

5. Application Scenarios and Values

The purpose-driven artificial consciousness service system built in this project has universal modular capabilities, which can be widely used in multiple scenarios and create significant value for various industries:

Wisdom Education: In the field of education, the system can act as an intelligent personality mentor. By modeling students' purpose and cognitive state, we provide interactive teaching services that teach students according to their aptitude. For example, when students ask questions during the learning process, the system not only gives answers, but also tracks students' mastery of knowledge points, and adjusts the depth of explanations or the difficulty of practice questions in a timely manner. For confusion in learning, the system can detect students' emotional frustration and give encouragement or hints to prevent them from losing motivation to learn. Compared with traditional "one-size-fits-all" online courses, this system allows each student to enjoy tailor-made tutoring to enhance learning efficiency and learning experience. At the same time, teachers can also use the system to automatically generate personalized learning reports to understand students' weak links, so as to carry out targeted offline tutoring and realize human-machine collaborative Wisdom teaching.

Intelligent customer service: In the field of enterprise customer service and customer experience, this system can be deployed as an intelligent customer service assistant. Using Purpose Recognition and Sentiment Analysis, the system can quickly understand the real demands behind the questions raised by customers (such as consultations, complaints, or purchase intentions), and invoke the knowledge graph to provide accurate and contextual responses. In the process of dialogue, the system adjusts the response strategy according to the customer's response: for the unsatisfactory response, the system further retrieves information or changes the expression mode through the cognitive feedback mechanism; For emotionally charged customers, the system will switch to reassuring mode or transfer the conversation to a human agent in a timely manner when it recognizes negative emotion. Thanks to its self-learning capabilities, the system can also continuously enrich the knowledge base and conversation strategies from historical customer service records. The application of this solution can significantly reduce the labor cost of enterprise customer service, improve response speed and customer satisfaction, and inject new vitality into scenarios such as Wisdom business hall and e-commerce customer service.

Government cognitive service: In government public service and decision support scenarios, the artificial awareness system can play a role as a government cognitive assistant. Faced with a large amount of information about policies, regulations and procedures, it is often difficult for the public to obtain the answers they need in a timely manner. The system provides accurate and detailed policy consulting services for citizens through the built-in authoritative government knowledge graph (covering policy documents, service guides, etc.) and the purpose-driven Q&A engine. For example, when a citizen asks for the materials needed to do something, the system not only lists them, but also automatically adjusts the focus of the answer according to the identity of the questioner (individual/enterprise), and asks for the default information to give customized guidance. Within the government, decision-makers can use the system to conduct data analysis and assist decision-making: the system can understand the governance goals that leaders are concerned about, extract useful information and knowledge from scattered government data, and form decision-making suggestions or risk early warning reports. Through the introduction of artificial awareness system, digital government will be more intelligent and humane, improving the efficiency of public services and scientific decision-making.

In addition, the technology of this project can also be extended to medical and health care (intelligent consultation and health advisory), financial consulting (intelligent investment advisory, risk assessment), and industrial operation and maintenance(intelligent fault diagnosis system) and other fields. All scenarios that require human-computer cognitive interaction can be driven by the purpose drive and cognitive feedback capabilities of the system to improve the quality of interaction and intelligence. For example, in medical scenarios, the system can be used as a doctor's assistant to analyze the symptoms described by the patient, give diagnostic suggestions based on the medical knowledge graph, and provide reminders and verification for the doctor's decision-making, so as to build a safe and controllable intelligent diagnosis and treatment model. In summary, the value of this project in various application fields is reflected in the following: improving the accuracy of information acquisition and decision-making, the naturalness and personalization of human-computer interaction, the credibility and explainability of system operation, and ultimately promoting the intelligent upgrading of the industry and the significant improvement of user experience.

6. Landing foundation and technical feasibility

This project has a solid R&D foundation and landing feasibility. In terms of technical prototypes, the team has developed a prototype system for core modules. For example, in the Wisdom education scenario, we built a prototype of intelligent teaching Q&A, which realized the functions of textbook Q&A and learning progress tracking based on knowledge graph. In terms of cognitive assessment of large language models, we released the Deepseek series of large models and their awareness level evaluation reports, which verified the effectiveness of the DIKWP system in cognitive analysis. These prototypes provide direct experience in building a complete system for a project.

In terms of data and knowledge base, the team has accumulated a large number of data resources and knowledge graph construction experience in vertical fields. Taking the field of education as an example, we have built a knowledge graph covering the main subjects of primary and secondary schools, including the association of tens of thousands of nodes of concepts and exercises, as the support of personalized teaching modules. In the field of customer service, we have compiled the FAQ corpus and product knowledge base of an e-commerce platform to train and test the purpose recognition and response accuracy of the customer service robot. This pilot data provides the initial training and validation basis for the system, making the implementation of customized applications more feasible.

In terms of patent technology transformation, the project benefited from a number of invention patents that have been authorized by the team as theoretical and engineering support. For example, the "Semantic Modeling and Abstraction Enhancement Method of Three-layer Graph Framework" proposed by the patent CN107038262B has been transformed and applied in our knowledge graph module for dynamic abstraction and improvement of knowledge structure. The "Whole Life Cycle Assessment Method of Software System Based on DIKWP Model" proposed by the patent CN114356770A was used as a reference to monitor the performance bottlenecks and optimization directions of each stage of system development. The patent CN113810480A "Emotional Communication Method for DIKW Content Objects" directly supports the design of the human-computer emotional interaction unit of the system. In addition, the team's patent achievements in privacy protection, blockchain and other directions (such as the application of DIKWP in privacy resource modeling and user purpose identification) also provide a guarantee for the security and scalability of the system. Such a combination of production and research ensures the advancement and reliability of the project technology - the maturity of the patent results proves the feasibility of the plan, and the authorization of the patent also lays the intellectual property foundation for future commercial promotion.

In terms of team ability, we have an interdisciplinary R&D team and perfect experimental conditions. The core technical personnel have an in-depth understanding of the DIKWP model and semantic intelligence technology, have participated in a number of related topics and system development, and have a plan to deal with possible engineering challenges (such as large-scale knowledge graph concurrent query, real-time purpose computing performance, etc.). At the same time, the team is equipped with a high-performance computing center and a cloud service platform, which can provide necessary computing power support and development and testing environment for this project. At present, we have built a prototype of the cloud deployment of the system in the laboratory environment, and the modules are integrated through APIs, and preliminary functional tests have been carried out on a small-scale user group, and the results show that the system can complete functions such as intelligent question answering, purpose recognition and feedback adjustment as expected, and the technical route is feasible.

In summary, the project is well prepared and feasible in terms of technology accumulation (prototype and data), intellectual property rights (patent transformation) and implementation team (manpower and equipment). Next, we will further integrate and improve the system functions based on the existing results, and carry out testing and verification on a larger scale to lay a solid foundation for the implementation of the project.

7. Team structure and accumulation of achievements

The project is led by Prof. Yucong Duan, President of the World Association for Artificial Consciousness, and his R&D team brings together experts and young talents in the fields of artificial intelligence, cognitive science, and educational technology. In terms of team structure, there are not only senior professors, doctoral students, but also experienced engineers, which realizes the close combination of theoretical research and engineering implementation. Professor Yucong Duan is the pioneer of the DIKWP model and the direction of artificial consciousness, and serves as the chairman of the International Artificial Intelligence DIKWP Assessment Standards Committee (DIKWP-SC) and a core member of the World Association for Artificial Consciousness (WACA), with deep insights and extensive influence in this field. The technical backbone includes a number of researchers who have obtained doctoral degrees, such as semantic modeling, natural language processing, and knowledge graph construction. In addition, the team invited pedagogical experts as consultants to ensure that the system design meets the needs of teaching practice. Efficient organization and multidisciplinary integration are a strong guarantee for the team to complete this project.

In terms of accumulation of achievements, the team has been deeply engaged in related fields for many years and has achieved a series of high-level research and application results:

In terms of theoretical research, the team has published/written a number of papers and reports with international influence, such as "Preliminary Semantic Mathematics of DIKWP" and research report on artificial intelligence semantic communication, etc., and systematically expounded the mathematical and personalized semantic communication of the DIKWP model. In 2025, the team will take the lead in releasing the world's first white-box evaluation report on the awareness level of large models, leading a new direction for cognitive intelligence evaluation. These research works have laid the academic foundation of the project and have been highly praised by peers in the industry.

In terms of patents and inventions, the team has obtained more than 100 national invention patents authorized or published, covering key artificial intelligence technology fields such as semantic understanding, knowledge processing, privacy protection, and blockchain. These patents underpin the various components of the DIKWP model. For example, as early as 2017, the team proposed the application method of data/information/knowledge three-layer graph architecture in semantic search and situational awareness. In recent years, it has expanded to emerging topics such as purpose-driven content transmission, privacy protection, and blockchain consensus. The diversified patent layout not only reflects the breadth and depth of the team's technological innovation, but also shows that we have independent and controllable intellectual property reserves for the technologies related to this project.

In terms of system and platform, the team has previously developed the "DeepSeek" large model and the supporting cognitive search engine prototype, and explored the application of DIKWP theory to the field of intelligent search and Q&A. The relevant achievements have been recognized in many domestic and foreign innovation competitions, and cooperation pilots have been carried out with enterprises. These experiences have given the team a very familiar understanding of the process from theoretical algorithms to practical systems. At the beginning of this project, these existing modules (such as knowledge graph services, dialogue interfaces, etc.) have been integrated for secondary development, which has greatly accelerated the research and development progress.

Overall, the project team has a combination of top-notch theoretical innovation capabilities and rich practical engineering experience. A good cooperation atmosphere and problem-solving mechanism have been formed within the team, which can efficiently respond to various challenges encountered in the promotion of the project. We are confident in our technology accumulation and collaboration capabilities, and we firmly believe that we can successfully move forward with this project and achieve the expected goals.

8. Future planning and commercialization scenarios

In terms of future planning, the team will gradually promote technology improvement, scenario expansion and business transformation:

Technology Deepening and Iteration: In the next stage, we will focus on improving system performance and functional completeness. On the one hand, we continue to optimize the recognition accuracy and response speed of Purpose, and introduce methods such as larger-scale pre-trained models and reinforcement learning to make the system's understanding of complex Purpose more accurate and the contextual memory of multi-round conversations more durable. On the other hand, the cognitive feedback mechanism should be enriched, such as combining multimodal perception (camera captures facial expressions, physiological sensing, etc.) to more comprehensively evaluate the user's cognitive state, so as to improve the reliability of feedback. We also plan to further engineer the semantic mathematics and knowledge reasoning modules to enhance the inference efficiency of the system under a large-scale knowledge base. At the same time, it actively participates in the standardization of artificial intelligence, and incorporates DIKWP models and white-box evaluation indicators into industry standard proposals to consolidate its leading position in technology.

Pilot application expansion: On the basis of mature technology, we plan to cooperate with leading users in various fields to carry out pilot projects. The short-term goal is to cooperate with some schools or online education platforms to deploy the teaching and tutoring module of the system in the field of Wisdom education to verify its effect on improving student achievement and engagement. In the field of government services, one or two city government units were selected to trial run the intelligent consulting assistant to collect feedback from the public. Through these pilots, we will gain valuable real-world application data to further refine our products and make them more relevant to our business needs. In the medium and long term, it will gradually expand to high-value fields such as medical care and finance, establish cooperation with leading enterprises in the industry, and let the artificial awareness service system be implemented in a wider range of scenarios to create greater value for the society.

Commercialization path: The project has clear commercialization prospects and diversified profit models. First of all, we plan to build the system into a general artificial awareness service platform, which will be open to B-end customers using a subscription system or a cloud service (XaaS) model that charges by the number of calls. Educational institutions, enterprise customer service centers, government 12345 hotlines, etc., can connect the system to their business processes through APIs to achieve low-threshold use. Secondly, for the needs of specific industries, we will launch customized solutions to provide privatized deployment services that are more suitable for the industry through secondary development to obtain project income. In addition, with the team's patents and technical reserves, we are also considering incubating start-up companies or joint ventures with existing leading enterprises for product operation. For example, we have partnered with a well-known edtech company to launch an "AI Personalized Tutor" application, or partnered with a cloud service provider to embed our intelligent customer service engine into their SaaS product system. At the same time, we will actively strive for special government funds and industrial investment to help the rapid growth of the project. In the process of business promotion, we attach great importance to data security and ethics, and ensure that the system complies with relevant laws and regulations (such as education and privacy protection regulations, etc.) to gain the trust of users and regulators.

Outlook for social benefits: From a more macro perspective, this project is in line with the country's strategic direction for the independent innovation and application of a new generation of artificial intelligence technology. We expect that the wide application of the project results will help promote educational equity (making up for regional teacher disparities through AI personalized teaching), improving the quality of public services (smart government lowers the threshold for people to do things), and promoting the development of the digital economy (reducing costs and increasing efficiency of enterprise customer service). The team will also continue to explore the innovative application of artificial consciousness technology in more areas of people's livelihood, and contribute to the construction of China's AI industry and digital civilization.

To sum up, the project has completed a thorough planning layout: consolidating technology and verifying value in the short term, expanding the market to form a business model in the medium term, and leading the innovation of AI service paradigm in the long term. We firmly believe that with the support of all parties, the "Purpose-driven and Cognitive Feedback Artificial Awareness Service System" will take root and have a far-reaching industrial and social impact. The project team will go all out to achieve this goal with full enthusiasm and rigor.

References:

1. CN114357169A. Blockchain Construction Method Based on DIKWP Model for Essence-Semantic Computation and Inference.

2. CN107038262B. A semantic modeling and abstraction enhancement method based on data graph, information graph and knowledge graph framework based on correlation frequency calculation

3. Science and Technology Daily. Large Language Model Awareness Level "Knowledge Quotient" White Box DIKWP Evaluation 2025 Report Released.

4. Yucong Duan, et al. Transforming the symbolic concept space into a DIKWP semantic space to realize personalized semantic communication.

5. Yucong Duan, et al. Research report on cross-domain application based on Yucong Duan's semantic mathematical theory.

6. CN113810480A. Emotional communication method based on DIKW content objects.

7. CN113645284A. Purpose-driven multimodal DIKW content transmission method. CN114356770A. A life-cycle assessment method for software systems based on DIKWP model for intrinsic computing