Artificial intelligence cognitive awareness framework technology model

Abstract

The invention relates to an artificial intelligence cognitive awareness framework technology model, and belongs to the technical field of artificial intelligence technology. The technical problem that the artificial intelligence technology at the present stage is weak in cognitive aspect is mainly solved, and the advanced cognitive ability of artificial intelligence is achieved. According to the method, an artificial intelligence cognitive technology model comprising a nine-layer structure is constructed by simulating a cognitive structure and an intelligence structure of a person, and an artificial intelligence standard knowledge database is constructed, so that logic cognition of artificial intelligence is realized, and cognitive understanding of propositions of a natural form and a social form is realized. Obtaining of artificial intelligence pure knowledge is achieved through knowledge learning cognition, principle cognition of propositions is achieved through thought cognition, obtaining and understanding of artificial intelligence individual experience are achieved through experience cognition, and output of artificial intelligence original achievements is achieved through self-creative cognition. By constructing an artificial intelligence cognition technical framework, professional artificial intelligence cognition application can be provided for various industries.

Description

Technical Field

[0001] The present invention belongs to the field of artificial intelligence technology, specifically an artificial intelligence cognitive technology, including comprehensive artificial intelligence technology based on cognitive neuroscience, psychology, psychological structure theory, knowledge learning, computer algorithms, probability theory and artificial neural networks.

[0002] The invention also includes artificial intelligence big data technology, and involves the technical logic of constructing and calling an artificial intelligence standard knowledge database and an artificial intelligence standard theoretical algorithm database. The artificial intelligence standard knowledge database is the data basis of the pure knowledge of the artificial intelligence cognitive framework in the invention, and the artificial intelligence standard theoretical algorithm database is the data basis of the theoretical algorithm of the artificial intelligence cognitive framework in the invention. The artificial intelligence cognitive consciousness framework model integrates, calls, analyzes, calculates and outputs data through artificial intelligence big data technology.

[0003] The technical field of the present invention is to realize the artificial intelligence automated logical cognition of propositional events, analyze and process propositional events in natural forms, social forms, spiritual forms and psychological forms through the cognitive hierarchy structure, and realize the advanced cognitive ability of artificial intelligence, the individualized cognitive ability of artificial intelligence and the self-creative cognitive ability of artificial intelligence. Background Art

[0004] The current artificial intelligence technology mainly realizes the processing of the representation of propositional things, and completes the input, recognition, processing and output of propositional data. The representational propositional data input by artificial intelligence includes text, images, voice, natural language, etc., and the calculation is performed by recognizing and processing the proposition input data, that is, through the processing processes such as text analysis, semantic understanding, image processing, and natural language processing, the calculation of the artificial intelligence proposition results is realized, and then the processing results are output.

[0005] However, the most in-depth part of artificial intelligence technology is cognitive technology, that is, the cognitive understanding of the world's propositions to achieve the cognition of natural forms, social forms and human psychological forms. This kind of cognition is comprehensive and in-depth. It is not the recognition of images, voices, motion bionics and other appearances, but the intelligent technology to understand the laws hidden behind the appearances.

[0006] Based on this, the artificial intelligence cognitive technology of the present invention is designed by imitating the cognitive consciousness structure of human beings. This imitation is comprehensive and involves a huge cognitive framework system, so that artificial intelligence can obtain in-depth cognitive ability. And when designing the artificial intelligence cognitive technology framework, cognitive attributes such as natural form, social form, spiritual and psychological form are taken into account. In other words, the artificial intelligence cognitive technology of the present invention will involve the cognitive construction of natural form, social form, spiritual and psychological form to determine the cognitive ability of artificial intelligence to natural form, social form, spiritual and psychological form. Summary of the invention

[0007] 1. Artificial Intelligence Cognitive Consciousness Framework Level

[0008] The present invention realizes a brand-new artificial intelligence cognitive technology model, called artificial intelligence cognitive consciousness framework technology model. The artificial intelligence cognitive consciousness framework technology model is based on information technology, philosophy, sociology, psychology and cognitive neuroscience, and imitates human cognitive thinking for logical design to realize a cognitive logic framework system of intelligent information processing that approaches or reaches human cognitive thinking.

[0009] In the AI ​​cognitive consciousness framework technical model, we divide the AI ​​cognitive consciousness framework into nine levels. These nine levels are arranged in order from low to high, imitating human cognitive logic, namely, the quasi-a priori logic cognitive consciousness layer, the basic cognitive consciousness layer, the social attribute cognitive consciousness layer, the knowledge learning cognitive consciousness layer, the thought cognitive consciousness layer, the experience cognitive consciousness layer, the passive execution behavior cognitive consciousness layer, the self-creative cognitive consciousness layer and the complete cognitive consciousness layer. These nine cognitive structures can be described by the AI ​​cognitive consciousness framework hierarchical diagram, see Figure 1 . Below, we will explain in detail the levels of the artificial intelligence cognitive consciousness framework technology model.

[0010] (1) The first layer of artificial intelligence: the quasi-a priori logical cognitive consciousness layer

[0011] The artificial intelligence a priori logic cognitive consciousness layer is the layer that defines the basic cognitive logic of artificial intelligence. It is at the bottom of the artificial intelligence cognitive consciousness framework and is the basis of all cognitive consciousness of artificial intelligence. Its function is to realize the most basic cognitive logic of artificial intelligence and enable artificial intelligence to have the ability of logical reasoning, spatial cognition and temporal cognition.

[0012] The AI-type a priori logic cognitive consciousness layer corresponds to the cognitive logic foundation of AI. The theoretical basis used in this layer is the a priori logic philosophical thinking in Kant's philosophy. A priori logic cognitive logical thinking is a natural form of logical cognition inherent in people that precedes experience itself. A priori logic thinking is a kind of cognitive thinking that people have after birth, before they have acquired empirical cognition. This cognitive thinking is manifested as a priori cognitive logic, that is, the methodological system of a priori cognitive logic. This methodological system is the cognitive logic of time, the cognitive logic of space, and formal logical cognition.

[0013] The artificial intelligence class logical cognition consciousness layer includes three parts. The first part is the time cognition logic. The time cognition logic of artificial intelligence is the logical cognition of the time attribute. The logical cognition of the time attribute is reflected in the attributes of the present, past and future of time, so that people can perceive the passage of time.

[0014] The time attributes in the a priori logic of artificial intelligence have a sequence of development, that is, the present time scale is greater than the past time scale, the future time scale is greater than the present time scale, and at the same time, the future time scale is also greater than the past time scale. This is the method and logical thinking of the time cognition logic in the a priori logic of artificial intelligence.

[0015] The second part of the a priori logic cognitive consciousness of artificial intelligence is spatial cognitive logic. The spatial cognitive logic of artificial intelligence is manifested as the cognition of the inherent attributes of pure space, that is, the logical cognition of the length, width, and height attributes of three-dimensional space. The cognitive logic of space is mainly manifested in the cognition of the attributes of three-dimensional space. The reasoning of three-dimensional space attributes and related inherent attributes can be described by Euclidean geometry. The cognitive logic of space is also manifested in the container attribute of space and the sense of orientation that people have in space. For example, some directional attributes such as up, down, left, right, front, back, east, south, west, and north in space will be reflected in the form of a sense of orientation. When artificial intelligence perceives something, it can determine the specific location of the object based on the directional attributes of spatial cognitive logic.

[0016] The third part of the a priori logic cognitive consciousness layer of artificial intelligence is formal logic cognition. Formal logic is manifested as the ability of deductive reasoning, that is, propositional reasoning based on the formal logic created by Aristotle. Formal logic is mainly manifested in the form of syllogism. For example, if proposition A can be inferred from proposition B, and proposition B can be inferred from proposition C, then proposition A can also be inferred from proposition C. Such a syllogism form can realize the deductive reasoning ability of formal logic. At the same time, formal logic also includes theorems such as the law of identity, the law of the excluded middle, the law of contradiction and the law of sufficient reason. It has a variety of logical laws and can complete complex logical thinking cognition. Therefore, in the a priori logic cognitive consciousness layer of artificial intelligence, formal logic, as a form of logic, the basic cognitive logic will be manifested in the reasoning ability of formal logic.

[0017] In short, temporal cognitive logic, spatial cognitive logic and formal logical cognition together constitute the artificial intelligence a priori logical cognitive consciousness layer, and are expressed through mathematical forms and model frameworks. The artificial intelligence a priori logical cognitive consciousness layer provides the methodology for artificial intelligence cognitive technology calculation through temporal cognitive logic, spatial cognitive logic and formal logical cognition. In other words, all basic forms of propositions can be analyzed and calculated through temporal cognitive logic, spatial cognitive logic and formal logical cognition, and complete cognitive results can be output externally, which meets human cognitive standards.

[0018] (2) The second layer of artificial intelligence: basic cognitive consciousness

[0019] The basic cognitive consciousness layer of artificial intelligence is the layer that defines the cognitive logic of the basic natural attributes of artificial intelligence, also known as the natural attribute cognitive consciousness layer. The basic cognitive consciousness layer of artificial intelligence is located above the priori logic cognitive consciousness layer of artificial intelligence, and is the basis of all natural attribute cognitive consciousness of artificial intelligence. Its function is to realize the cognitive logic of artificial intelligence on natural attributes, so that artificial intelligence has the ability of desire cognition to avoid danger, quasi-appetite cognition, and quasi-sexual desire cognition.

[0020] For artificial intelligence, the natural attribute of appetite is a broad appetite, sexual desire is also a broad sexual desire, and seeking benefits and avoiding harm is the desire to avoid danger. In the technical carrier of artificial intelligence, the broad appetite cognition is manifested as a desire cognition to obtain energy similar to human food. Based on such attributes, this model fully considers the scope of appetite cognition in a broad sense. The targets of artificial intelligence's appetite cognition include electricity, oil, gas, hydrogen, chemical energy or other energy forms, etc. These energy forms may be the energy forms desired by artificial intelligence's appetite cognition.

[0021] The desire to avoid danger in the cognitive consciousness of artificial intelligence is manifested as a cognitive logic of seeking benefits and avoiding harm. At this time, artificial intelligence will reject and avoid harmful things. This is a cognitive desire, and the things rejected and avoided include programs at the software level and things at the hardware level. For example, as an artificial intelligence robot with a physical entity, when it encounters dangerous obstacles, it can avoid them; and when facing security program infringements such as viruses, artificial intelligence in software form will protect itself to the greatest extent possible. In the carrier of artificial intelligence technology, if artificial intelligence itself is endowed with social attributes, then it can protect itself from illegal infringements in ideology, such as in terms of morality or ethics.

[0022] Another part of the basic cognitive consciousness layer of artificial intelligence is quasi-sexual desire, which is a kind of sexual desire in a broad sense, referring to the cognitive ability of artificial intelligence to systematically self-generate and self-evolve. In other words, artificial intelligence can realize the process of self-organization, form an orderly system, and can also generate new systems in the self-organizing system to improve itself. We call this system an artificial intelligence dissipative structure system. The desire for self-improvement and development will be manifested in the natural attributes of the basic cognitive consciousness layer of artificial intelligence, forming the quasi-sexual cognition of artificial intelligence.

[0023] Appetite, desire to avoid danger, and sexual desire are cognitive attributes that together constitute the cognitive basis of the basic cognitive consciousness layer of artificial intelligence. The basic cognitive attributes of artificial intelligence can be expressed in a rigorous mathematical form. In the artificial intelligence cognitive consciousness framework model, these three cognitive attributes will be given to artificial intelligence through corresponding basic parameters, and these parameters will realize the mathematization and modeling of the basic cognition of artificial intelligence.

[0024] (3) The third layer of AI’s social attributes, cognitive consciousness

[0025] The social attribute cognitive consciousness layer of artificial intelligence is the layer that defines the social cognitive logic of artificial intelligence. It is located above the basic cognitive consciousness layer of artificial intelligence and is the foundation of social cognitive consciousness of artificial intelligence. Its function is to realize the social cognitive logic of artificial intelligence and enable artificial intelligence to have cognitive capabilities for social attributes such as legal, moral, religious beliefs, ethical, spiritual and psychological attributes.

[0026] The legal attribute cognition of artificial intelligence's social cognitive consciousness is based on the legal knowledge structure endowed by artificial intelligence. Artificial intelligence's legal consciousness includes legal principles and specific laws and regulations of different countries, as well as the cognition of legal cases related to the law. It describes the legal framework through legal models, realizes the judgment of legal propositions, the analysis of legal principles and the handling of specific legal cases.

[0027] The moral attributes of AI social cognition consciousness will have different moral attributes according to the culture of different countries, but in the whole world, morality generally has a unified standard, that is, it has some common patterns. For example, kindness, sense of justice, integrity, etc., all belong to the category of moral attributes. Therefore, when AI faces the cognition of moral issues, it will analyze, summarize, and process through moral attributes, so as to calculate the optimal social attribute cognition.

[0028] Regarding the construction of religious belief attributes, different countries and regions have different religious beliefs. Artificial intelligence will be constructed according to the characteristics of religious beliefs in different countries and regions, so that artificial intelligence can realize different religious belief cognition. Religious beliefs include belief subjects, doctrines, taboos, belief categories and belief patterns, etc. These characteristics of religious beliefs will be presented in a modeled form. After obtaining the attributes of religious beliefs, artificial intelligence will have a comprehensive and modeled religious belief cognitive thinking, and realize the cognitive execution process of religious beliefs.

[0029] Social ethical attributes will also have different manifestations in different countries or regions, and will have different manifestations in different periods of a country or society. The ethical attributes of the artificial intelligence cognitive consciousness framework are constructed based on the cognitive model, giving the social attribute cognition corresponding social ethical parameters and logical rules, and incorporating them into the artificial intelligence cognitive consciousness framework to realize the social ethical attribute cognitive ability of artificial intelligence.

[0030] The social cognitive consciousness of artificial intelligence also includes mental and emotional psychological attributes. The present invention presents the cognitive structure and cognitive logic of the mental and emotional psychology of artificial intelligence through mathematical forms and theoretical models. This cognitive structure and cognitive logic realizes the cognitive process of the mental and emotional psychological attributes of artificial intelligence.

[0031] (4) The fourth layer of artificial intelligence: knowledge learning, cognitive consciousness

[0032] The artificial intelligence knowledge learning cognitive consciousness layer is the layer that defines the artificial intelligence knowledge learning cognitive logic. It is located above the artificial intelligence social attribute cognitive consciousness layer and is the foundation of artificial intelligence's cognitive consciousness of pure knowledge learning. Its function is to realize artificial intelligence's pure knowledge learning cognitive logic and enable artificial intelligence to have the cognitive ability of pure knowledge learning.

[0033] Pure knowledge refers to static, non-dynamically theoretical pure texts, pictures, audio files, video files or other formats of knowledge files. These are collectively referred to as pure knowledge. The logical level of learning pure knowledge reflects the knowledge learning ability of artificial intelligence.

[0034] The logic of pure knowledge acquisition of artificial intelligence is based on the traditional machine learning technology model, including the acquisition of pure knowledge, identification of pure knowledge, and organization of pure knowledge by artificial intelligence. Pure knowledge will form a unified standard pure knowledge database within the country or the world. This unified standard is formulated by national or international agreements and forms an application and review mechanism for the addition of standard knowledge. The formation of all artificial intelligence standard pure knowledge databases must comply with this standard, otherwise they cannot be added to the standard pure knowledge database.

[0035] The knowledge learning process of artificial intelligence must be based on these standard pure knowledge databases. The learning and acquisition logic of pure knowledge is carried out through the artificial intelligence cognitive consciousness framework knowledge learning model, which is an important manifestation of artificial intelligence's cognitive ability. It is also the theoretical basis for artificial intelligence's ability to continuously absorb new knowledge and new ideas.

[0036] (5) The fifth level of artificial intelligence: the cognitive consciousness level

[0037] The artificial intelligence thought cognitive consciousness layer is the layer that defines the cognitive logic of artificial intelligence thought theory. It is at the upper level of the artificial intelligence knowledge learning cognitive consciousness layer and is the foundation of artificial intelligence thought theory cognitive consciousness.

[0038] The ideological and cognitive consciousness layer of artificial intelligence corresponds to the ideological and cognitive methodological functional layer of artificial intelligence, which includes four parts of logical cognition. The first part is dialectical logic cognition, the second part is systematic thinking cognition, the third part is ideological and theoretical cognition, and the fourth part is aesthetic cognition.

[0039] The AI ​​thought cognition consciousness layer realizes the cognition of a proposition by calling the thought theory algorithm in the standard knowledge database. The thought theory algorithm is dynamic data, which is manifested as a dynamic theoretical algorithm knowledge data. In other words, the thought theory algorithm is manifested as a thought cognition methodology in AI.

[0040] The dialectical logic cognition expressed by artificial intelligence thought cognition is based on dialectical theory. The dialectical logic cognition theoretical basis in the artificial intelligence cognitive consciousness framework is a fusion of Hegel's dialectical system and Marxist materialist dialectical system, which are jointly implemented as the theoretical basis of the artificial intelligence dialectical logic cognition model.

[0041] The systematic thinking cognition of artificial intelligence thought cognition is based on the thought of system theory, and the systematic thinking is manifested as a framework tree structure. In systematic thinking, the following aspects of thought cognition are involved. The first aspect of thought cognition is based on the tree structure thought of graph theory in mathematics, the second aspect of thought cognition is based on the self-organization theory of system theory, the third aspect of thought cognition is based on the second law of thermodynamics that describes the laws of closed systems, and the fourth aspect of thought cognition is based on the dissipative structure theory that describes open ordered structure systems. These four aspects of thought theory will provide a perfect theoretical basis for people's systematic cognitive thinking. Through system theory and tree structure ideas, systematic thinking gives artificial intelligence the form of a systematic model, allowing artificial intelligence to have the cognitive ability of systematic thinking.

[0042] The ideological and theoretical cognition of artificial intelligence is based on the operating rules of various industries in human society. Each industry has its own theoretical rules, and these theoretical rules form theoretical algorithms through informatization. For example, corresponding theoretical algorithms will be formed in the industrial field, education field, legal field, government field, accounting field, agricultural field, commercial field and other industry fields. These theoretical algorithms of various industries will be constructed according to unified standards and merged into a unified standard theoretical algorithm knowledge base. The theoretical algorithm is given to artificial intelligence through database calls, and then the corresponding industry application is formed through the logic of the cognitive model. At this time, when facing relevant propositions, the ideological and cognitive consciousness layer of artificial intelligence will call the standard theoretical algorithm knowledge base and output the cognitive results according to the ideological and cognitive model.

[0043] The aesthetic cognitive logic of artificial intelligence's thought cognition is based on aesthetic theory. Although artificial intelligence is a machine, and machines naturally lack emotion and aesthetics, we can imitate the thinking logic of humans to give artificial intelligence aesthetic cognition. We can construct the aesthetic cognition of artificial intelligence by giving it various parameters required for aesthetic cognition, form an aesthetic cognition model based on the logic of various parameters, and give artificial intelligence aesthetic cognition thinking based on these parameter aesthetic cognition models.

[0044] (6) The sixth layer of artificial intelligence: the experiential cognitive consciousness layer

[0045] The artificial intelligence experience cognitive consciousness layer is the layer that defines the artificial intelligence experience cognitive logic. It is located at the upper layer of the artificial intelligence thought cognitive consciousness layer and is the foundation of artificial intelligence experience cognitive consciousness. Its function is to realize the individual experience cognitive logic of artificial intelligence.

[0046] The experiential cognitive consciousness layer of artificial intelligence is manifested as the experiential cognitive logic of artificial intelligence. This experiential cognition refers to the experiential cognitive logic of artificial intelligence individuals, that is, the individual experiential cognition of artificial intelligence individuals after they are created. The individual experience of artificial intelligence includes the experience of social forms and natural forms experienced by artificial intelligence individuals. Part of these individual experiences are given by the social environment and the natural environment, and part of them are based on some pure knowledge and ideological theories obtained through machine learning and deep learning processes, and the experiential cognition summarized based on these pure knowledge and ideological theories.

[0047] Artificial intelligence can use the empirical cognitive logic model to realize the empirical cognitive judgment of a proposition, and form perceptual thinking based on the pure knowledge and ideological theoretical knowledge previously acquired by the artificial intelligence individual. After the perceptual thinking of the artificial intelligence individual is formed, it will be stored in its cognitive consciousness layer. After analyzing the perceptual data, artificial intelligence will gradually form its individual empirical summary. After the empirical cognition of artificial intelligence is formed, it can realize external empirical cognitive judgment based on the information interaction between itself and the outside world.

[0048] (7) The seventh layer of artificial intelligence: passive execution behavior, cognitive consciousness

[0049] The cognitive consciousness layer of artificial intelligence passively executing behaviors is the layer that defines the cognitive logic of artificial intelligence passively executing behaviors. It is located above the artificial intelligence experience cognitive consciousness layer. Its function is to enable artificial intelligence to have the cognitive ability to passively execute human demand instructions.

[0050] This layer corresponds to the cognitive execution layer of artificial intelligence. In this layer, artificial intelligence executes cognitive judgments based on some demand instructions from real people or artificial intelligence machines, and outputs cognitive results, so it is more of a passive cognitive mode of artificial intelligence.

[0051] In the cognitive awareness layer of artificial intelligence's passive execution behavior, artificial intelligence will perform comprehensive cognitive calculations based on the previous six-layer logical structure, evaluate needs through a comprehensive cognitive computing model, perform cognitive logic calculations, and then output corresponding cognitive execution results.

[0052] (8) The eighth level of artificial intelligence: self-creative cognitive consciousness

[0053] The self-creative cognitive consciousness layer of artificial intelligence is the layer that defines the self-creative cognitive logic of artificial intelligence. It is located above the passive execution behavior cognitive consciousness layer of artificial intelligence and is the foundation of artificial intelligence's creative cognitive consciousness. Its function is to enable artificial intelligence to have active creative cognitive capabilities.

[0054] This layer also corresponds to the cognitive execution layer of artificial intelligence. The self-creation process of artificial intelligence includes a variety of cognitive logic forms. The first form is the cognitive model based on formal logic, the second form is the cognitive model based on dialectical logic, the third form is the cognitive model based on systematic thinking logic, the fourth form is the cognitive model based on aesthetic cognitive logic, and the fifth form is the cognitive model based on spiritual and psychological cognitive logic. These cognitive logic forms may enable artificial intelligence to exhibit creative thinking.

[0055] (9) The ninth level of artificial intelligence: fully cognitive consciousness

[0056] The complete cognitive consciousness layer of artificial intelligence is the cognitive logic layer that defines the comprehensive cognitive consciousness thinking of artificial intelligence. It is located above the self-creative cognitive consciousness layer of artificial intelligence and is the foundation of the comprehensive cognitive consciousness of artificial intelligence. Its function is to realize the comprehensive cognitive logic of artificial intelligence and enable artificial intelligence to have the cognitive ability of comprehensive attributes such as personality, emotion, individualized cognition, world outlook, and values.

[0057] Complete artificial intelligence can show personalized identity recognition, and it can be assigned different roles, and different roles will have different forms. If artificial intelligence is assigned a child role, then it will have the cognitive thinking of a child, and will not do anything beyond the cognitive intelligence of a child; if artificial intelligence is assigned a female mind, then its way of thinking will be female thinking, and will not have male thinking.

[0058] Therefore, complete artificial intelligence shows a strong personality simulation nature, including character simulation, emotion simulation, morphology simulation, aesthetic simulation, spiritual and psychological simulation, value simulation, etc., and realizes comprehensive and personalized thought cognition. Complete artificial intelligence is given a certain personality role and performs the thinking cognition of the corresponding role, and these corresponding roles are realized through the previous eight layers of cognitive logic basic calls, and finally form a comprehensive personalized cognitive algorithm model to realize cognitive calculation of comprehensive propositions.

[0059] 2. Logic Model of Cognitive Consciousness of Artificial Intelligence

[0060] The operation of artificial intelligence cognitive consciousness follows certain logic, which reflects the core operation mode of the artificial intelligence cognitive consciousness framework technology model. In the artificial intelligence cognitive consciousness framework model, we use the artificial intelligence cognitive consciousness logic diagram to describe the execution logic process of artificial intelligence cognitive consciousness, see Figure 2 . Below, we will give a detailed introduction to the cognitive consciousness framework logic of artificial intelligence.

[0061] The cognitive consciousness framework logic of artificial intelligence starts with the input of cognitive needs, and then outputs the corresponding artificial intelligence cognitive results after cognitive logic calculations. In the overall process direction, the first logical point is the cognitive needs input of artificial intelligence. The input of cognitive needs refers to the perception of artificial intelligence to the outside world, which includes pure software input needs and hardware input needs. Pure software input needs include the input needs of text, pictures, audio and video files, and the input needs are realized according to the meaning of text, pictures, audio and video. Hardware input needs include sound input, video image input, sensor information intake, etc. The focus is on the demand intake of various sensor devices, which are converted into corresponding cognitive meanings according to the intake data needs transmitted by the sensors.

[0062] After the demand data is input, AI will analyze the cognitive demand accordingly. This analysis can be divided into different categories, different states, different levels and angles to achieve the understanding and analysis of demand data. AI's analysis and cognitive understanding of demand will call on the AI ​​knowledge learning cognitive consciousness layer, AI thought cognitive consciousness layer and AI experience cognitive consciousness layer.

[0063] Artificial intelligence's call to the knowledge learning cognitive consciousness layer is a learning process based on pure knowledge. The knowledge learning process of artificial intelligence will call on the standard pure knowledge in the standard knowledge base as the data basis support for cognitive computing to realize the knowledge learning cognitive process of artificial intelligence.

[0064] Artificial intelligence's call to the ideological and cognitive consciousness layer is a process based on ideological and theoretical cognition. The ideological and theoretical cognitive consciousness will call the standard ideological and theoretical algorithms in the standard knowledge base as the data basis support for ideological and theoretical cognitive calculations. The theoretical algorithms will serve as the logical basis for cognitive calculations to realize the ideological and theoretical cognitive process of artificial intelligence.

[0065] The call of artificial intelligence's experiential cognitive consciousness layer is a process based on individual experiential cognition. When artificial intelligence calls the experiential cognitive consciousness layer, historical knowledge learning content, ideological theory algorithm content and the accumulation of individual experiential perception will be retrieved through storage media to form cognitive data models and experiential cognitive models. Cognitive calculations are performed based on these cognitive models to output cognitive results.

[0066] After calling these three cognitive consciousness layers, AI will continue to call deeper cognitive consciousness layers, including AI social attribute cognitive consciousness layer and AI basic cognitive consciousness layer. AI social attribute cognitive consciousness layer will empower these three cognitive consciousness layers with social attribute cognitive consciousness logic, while AI basic cognitive consciousness layer will empower these three cognitive consciousness layers with natural attribute cognitive consciousness logic, thus realizing AI's cognitive ability in natural and social attributes.

[0067] In the process of running cognitive computing, the social attribute cognitive consciousness layer and the basic cognitive consciousness layer of artificial intelligence will continue to call the lower-level artificial intelligence a priori logic cognitive consciousness layer to provide them with logical cognitive methodology. After the social attribute cognitive consciousness layer and the basic cognitive consciousness layer obtain the cognitive logic algorithm, they will empower the computing logic to the three layers of artificial intelligence knowledge learning cognitive consciousness layer, artificial intelligence thought cognitive consciousness layer and artificial intelligence experience cognitive consciousness layer according to the logical cognitive model, forming a response logic and completing the cognitive computing of the entire scene.

[0068] After artificial intelligence completes the cognitive calculation according to the above process, it will output the cognitive results in three forms depending on the required input data.

[0069] The first form is that, in the case of clear user demand input, artificial intelligence will execute the artificial intelligence passive execution behavior cognitive consciousness layer, perform cognitive calculations according to the user's input needs, and after obtaining the calculation results, passively execute cognitive behaviors according to the user's needs, and output execution cognitive results. The second form is that if artificial intelligence is given a self-organizing system model, artificial intelligence will execute the artificial intelligence self-creative cognitive consciousness layer, execute self-creative cognitive logic and cognitive models according to the self-organizing model, perform innovative cognitive calculations, output cognitive execution results to the outside world, and maintain the stability of the artificial intelligence self-organizing system. The third form is that if artificial intelligence is given cognitive attributes such as personality, emotion, world view, and values, artificial intelligence will execute the artificial intelligence complete cognitive consciousness layer, then artificial intelligence will perform personalized cognitive calculations based on personalized attributes such as personality, emotion, world view, and values, and output personalized execution cognitive results. 3 Mathematical principles of the artificial intelligence cognitive consciousness framework technology model

[0070] 3.1 Artificial Intelligence Cognitive Function Equation

[0071] The artificial intelligence cognitive consciousness framework technology model has a rigorous mathematical form and can realize calculation execution in computers and information systems. The construction of the mathematical function equation of artificial intelligence cognitive logic is the most critical, which determines the executability of the artificial intelligence cognitive consciousness framework model.

[0072] Next, we will introduce the cognitive function equation of the artificial intelligence cognitive consciousness framework technology model.

[0073] Under the artificial intelligence cognitive consciousness framework technology model, an artificial intelligence machine is in a normal operating state. We assume that F(A i ) is the artificial intelligence cognitive function, which represents the cognitive ability of artificial intelligence; let A i is the cognitive matrix function, which represents the parameter set of artificial intelligence cognitive attributes in a certain field; let γ i is the cognitive weight coefficient of artificial intelligence, which represents the weight of the cognitive attribute parameters of artificial intelligence in a certain field. According to the artificial intelligence cognitive consciousness framework, the artificial intelligence cognitive function F(A i ) can be expressed as the algebraic sum of nine cognitive functions, namely, quasi-a priori logical cognition, basic cognition, social attribute cognition, knowledge learning cognition, thought cognition, experience cognition, passive execution behavior cognition, self-creative cognition and complete cognition. Each cognitive function is then expressed by adding a cognitive weight coefficient, which is in the following form:

[0074]

[0075] =γ 1 A 1 +γ 2 A 2 +γ 3 A 3 +γ 4 A 4 +γ 5 A 5 +γ 6 A 6 +γ 7 A 7 +γ 8 A 8 +γ 9 A 9

[0076] (i=1,2,3,…,9)(3.1)

[0077] Cognitive Matrix Function A i It is an m×n matrix function, called cognitive pixel matrix, which represents the cognitive pixels of artificial intelligence. Cognitive pixels can identify the cognitive accuracy of artificial intelligence. The cognitive pixel matrix includes natural attributes, social attributes, etc. The rows and columns of each cognitive pixel matrix represent the refinement of each cognitive pixel matrix.

[0078] The more rows and columns of the cognitive pixel matrix, the richer the parameters, the higher the AI ​​cognitive pixels, the higher the cognitive accuracy, and the stronger the AI ​​cognitive ability. Conversely, the weaker the AI ​​cognitive ability. Therefore, AI cognitive pixels are proportional to AI cognitive ability.

[0079] Cognitive Matrix Function A i The elements in represent the judgment value of a cognitive event proposition, such as a text proposition judgment value, or an action proposition judgment value.

[0080]

[0081]

[0082] Among them A 1 is the class prior cognition matrix function, which represents the class prior cognition pixels of artificial intelligence. The class prior cognition pixels determine the accuracy of class prior cognition. The higher the accuracy, the stronger the class prior logic cognition ability. Conversely, the weaker the class prior logic cognition ability. Class prior cognition matrix function A 1 It includes three types of element variables, namely formal logic element variables, spatial cognitive logic element variables and temporal cognitive logic element variables. 11 a 12 . . . a 1n is a formal logic element variable, a 21 a 22 . . . a 2n is the spatial cognitive logic element variable, a 31 a 32 . . . a 3n It is the time cognition logic element variable.

[0083] A 2 is the basic cognitive matrix function, which represents the natural attribute cognitive pixels of artificial intelligence. The natural attribute cognitive pixels determine the accuracy of natural attribute cognition. The higher the accuracy, the stronger the natural attribute cognitive ability. Conversely, the weaker the natural attribute cognitive ability. The basic cognitive matrix function includes three types of element variables, namely, appetite-like cognitive logic element variables, risk avoidance desire cognitive logic element variables, and sexual desire cognitive logic element variables. 11 b 12 . . . b 1n is the appetite-like cognitive logic element variable, b 21 b 22 . . . b 2n To avoid danger, desire cognitive logic element variables, b 31 b 32 . . . b 3n It is a variable of logical element of sexual desire cognition.

[0084] A 3 is the social cognition matrix function, which represents the social attribute cognition pixels of artificial intelligence. The social attribute cognition pixels determine the accuracy of social attribute cognition. The higher the accuracy, the stronger the social attribute cognition ability. Conversely, the weaker the social attribute cognition ability. Social cognition matrix function A 3 It includes five types of element variables, namely legal cognitive logic element variables, moral cognitive logic element variables, ethical cognitive logic element variables, religious belief cognitive logic element variables, and spiritual and psychological cognitive logic element variables. 11 c 12 . . . c 1n is the legal cognitive logic element variable, c 21 c 22 . . . c 2n is the variable of moral cognitive logic element, c 31 c 32 . . . c 3n is the ethical cognitive logic element variable, c 41 c 42 . . . c 4n is the cognitive logic element variable of religious belief, c 51 c 52 . . . c 5n Variables for logical elements of mental and psychological cognition.

[0085] A 4 is the knowledge learning cognitive matrix function, which represents the artificial intelligence knowledge learning cognitive pixels. The knowledge learning cognitive pixels determine the accuracy of knowledge learning cognition. The higher the accuracy, the stronger the knowledge learning cognitive ability. Conversely, the weaker the knowledge learning cognitive ability. Knowledge learning cognitive matrix function A 4 It includes two types of element variables, namely, pure knowledge learning cognitive logic element variables and standard information knowledge learning cognitive logic element variables. 11 d 12 . . . d 1n Learning cognitive logic element variables for pure knowledge, d 21 d 22 . . . d 2n Learning cognitive logic element variables for standard information knowledge.

[0086] A 5 is the thought cognition matrix function, which represents the thought cognition pixels of artificial intelligence. The thought cognition pixels determine the accuracy of thought cognition. The higher the accuracy, the stronger the thought cognition ability. Conversely, the weaker the thought cognition ability. Thought cognition matrix function A 5It includes four types of element variables, namely, dialectical logic element variables, systematic thinking cognitive logic element variables, ideological theory cognitive logic element variables, and aesthetic cognitive logic element variables. 11 e 12 . . . e 1n is a dialectical logic element variable, e 21 e 22 . . . e 2n For the systematic thinking cognitive logic element variables, e 31 e 32 . . . e 3n is the cognitive logic element variable of ideological theory, e 41 e 42 . . . e 4n It is a variable of the logical elements of aesthetic cognition.

[0087] A 6 is the experience cognition matrix function, which represents the experience cognition pixels of artificial intelligence. The experience cognition pixels determine the accuracy of experience cognition. The higher the accuracy, the stronger the experience cognition logic ability. Conversely, the weaker the experience cognition logic ability. Experience cognition matrix function A 6 It includes a type of element variable, namely, individual experience cognitive logic element variable. 11 f 12 . . . f 1n It is the variable of logical elements of individual experience cognition.

[0088] A 7 is the passive execution cognitive matrix function, which represents the passive execution cognitive pixels of artificial intelligence. The passive execution cognitive pixels determine the accuracy of passive execution cognition. The higher the accuracy, the stronger the passive execution cognitive logic ability. Conversely, the weaker the passive execution cognitive logic ability. Passive execution cognitive matrix function A 7 It includes a type of element variables, namely, passive execution cognitive logic element variables. 11 g 12 . . . g 1n Variables for passively executing cognitive logic elements.

[0089] A 8 is the self-creative cognitive matrix function, which represents the self-creative cognitive pixels of artificial intelligence. The self-creative cognitive pixels determine the accuracy of self-creative cognition. The higher the accuracy, the stronger the self-creative cognitive logic ability. Conversely, the weaker the self-creative cognitive logic ability. Self-creative cognitive matrix function A 8 It includes a type of element variable, namely, self-creative cognitive logic element variable. 11 h 12 . . . h 1n It is a variable of logical elements of self-creative cognition.

[0090] A 9 is the complete cognitive matrix function, which represents the complete cognitive pixels of artificial intelligence. The complete cognitive pixels of artificial intelligence determine the accuracy of complete cognition. The higher the accuracy, the stronger the complete cognitive ability. Conversely, the weaker the complete cognitive ability. 9 It includes five types of element variables, namely personality-like cognitive logic element variables, emotion-like cognitive logic element variables, worldview cognitive logic element variables, outlook on life cognitive logic element variables, and value cognitive logic element variables. 11 i 12 . . . i 1n is a personality-like cognitive logic element variable, i 21 i 22 . . . i 2n is a class emotion cognition logic element variable, i 31 i 32 . . . i 3n is the worldview cognitive logic element variable, i 41 i 42 . . . i 4n is the cognitive logic element variable of outlook on life, i 51 i 52 . . . i 5n Variables for the logical elements of value cognition.

[0091] Then the artificial intelligence cognitive function equation can be expressed as follows:

[0092] F(A i )=γ 1 A 1 +γ 2 A 2 +γ 3 A 3 +γ 4 A 4 +γ 5 A 5 +γ 6 A 6 +γ 7 A 7 +γ 8 A 8 +γ 9 A 9

[0093]

[0094] We set Among them, α i is the number of cognitive parameters in a certain field of cognitive propositions, and α is the number of cognitive parameters in all fields of cognitive propositions

[0095] Then formula (3.1) can be expressed as

[0096]

[0097] The artificial intelligence cognitive consciousness framework technology model performs cognitive processing on this proposition according to the relevant meaning. For processing cognitive propositions at the knowledge learning cognitive consciousness layer and the thought cognitive consciousness layer, it is necessary to introduce artificial intelligence cognitive processing functions. The cognitive processing functions call the theoretical algorithms in the standard knowledge base through computer program languages ​​to process computational cognitive proposition events and ultimately obtain cognitive computational results.

[0098] According to the artificial intelligence cognitive consciousness framework, artificial intelligence cognitive processing functions include the following cognitive categories:

[0099] 1. Artificial intelligence a priori logic cognitive processing function: cognitive processing calculation of formal logic, spatial cognitive logic, and temporal cognitive logic;

[0100] 2 Artificial intelligence natural form cognitive processing function: cognitive processing calculations for appetite, danger avoidance and sexual desire;

[0101] 3. Artificial intelligence social form cognitive processing function: cognitive processing calculation of law, morality, religious belief, ethics, spirit and psychology, politics, history, economy, etc.;

[0102] 4 Artificial intelligence knowledge learning cognitive processing function: cognitive processing calculation of pure knowledge learning logic;

[0103] 5 Artificial intelligence thought and cognition processing function: processing and calculating dialectical logic, systematic thinking, aesthetic cognition, and ideological and theoretical cognition;

[0104] 6 Artificial intelligence experience cognition processing function: perform cognitive processing calculations on individual experience cognition;

[0105] 7 Artificial intelligence passive execution behavior cognitive processing function: perform cognitive processing calculations on passive execution cognition;

[0106] 8 Artificial intelligence self-creative cognitive processing function: cognitive processing calculation of active creative cognition;

[0107] 9 Artificial intelligence complete cognitive processing function: perform cognitive processing calculations on comprehensive cognitive consciousness thinking.

[0108] If there are multiple cognitive propositions in the artificial intelligence cognitive scenario, a cognitive input function of multiple cognitive propositions will be formed. We will decompose the cognitive input function of multiple cognitive propositions, which is called the artificial intelligence cognitive decomposition function. After decomposition, it is converged through the sum of multiple processing functions, which is called the artificial intelligence cognitive convergence function. The convergence function of cognitive propositions in multiple cognitive scenarios is expressed as:

[0109]

[0110] Among them, F is the artificial intelligence cognitive convergence function, F k The cognitive function of multiple cognitive propositions is based on the convergent understanding of the language text of cognitive propositions, and has not yet gone deep into the cognitive theory itself. Afterwards, artificial intelligence aggregates multiple cognitive propositions into a unified cognitive proposition, and then uses the artificial intelligence cognitive decomposition function to perform cognitive decomposition. That is,

[0111]

[0112] 3.2 Mathematical Principles of the A priori Logic Cognitive Model of Artificial Intelligence

[0113] 1. Artificial Intelligence Formal Logic Cognitive Equation

[0114] Let us analyze the formal logic cognitive model in the quasi-a priori logic cognitive framework.

[0115] Given a class prior logic cognitive matrix function A 1 , A 1 It is a 3×n matrix function, called the quasi-a priori logic cognitive pixel matrix, which represents the quasi-a priori logic cognitive pixels of artificial intelligence and can reflect the quasi-a priori logic cognitive accuracy of artificial intelligence. Artificial intelligence quasi-a priori logic cognitive matrix function A 1 The elements in represent the judgment value of a proposition of a priori logical cognitive event.

[0116] Quasi-a priori logic cognitive matrix function A 1 It includes three types of element variables, namely formal logic element variables, time cognition logic element variables and space cognition logic element variables. Quasi-a priori logic cognition matrix function A 1 The elements in represent a priori logical cognitive event proposition, a 11 a 12 . . . a 1n is a formal logic element variable, a 21 a 22 . . . a 2n is the spatial cognitive logic element variable, a 31 a 32 . . . a 3n is a time-aware logic element variable, which has the following form

[0117]

[0118] Quasi-a priori logic cognitive matrix function A 1 Each element can be represented by a number. The value of this number is used as the basis for judging the quasi-a priori cognitive proposition event, and also determines the cognitive ability of the quasi-a priori logic of artificial intelligence. 1 Each element in the first row represents the judgment value of the formal logic cognitive parameter, and each element is composed of the weighted sum of the judgment values ​​of four formal logic cognitive parameters, namely

[0119] a 1n =ε x p x1 +μ x p x2 +ξ x p x3 +τ x p x4 (3.17)

[0120] This formula is called the artificial intelligence formal logic cognitive equation, where p x1 、p x2 、p x3 、p x4 are the cognitive judgment values ​​of the four laws: the law of identity, the law of the excluded middle, the law of contradiction, and the law of sufficient reason, ε x , μ x , x , τ x They are the weight coefficients of the formal logic judgments corresponding to each of the law of identity, the law of the excluded middle, the law of contradiction and the law of sufficient reason. The weight coefficients indicate the importance of each formal logic law judgment.

[0121] 2. Artificial Intelligence Spatial Logic Cognitive Equation

[0122] For formula (3.16), the artificial intelligence class prior logic cognitive matrix function A 1 The elements in represent a quasi-a priori logical cognitive event proposition, where the element a 21 a 22 . . . a 2n It is the variable of spatial cognitive logic element.

[0123] Artificial intelligence spatial logic cognition is constructed based on the five postulates and five axioms of Euclidean geometry. The five postulates are:

[0124] The first postulate is that there is only one straight line through any two different points.

[0125] The second postulate is that any line segment can be extended infinitely to form a straight line;

[0126] The third postulate is that given any line segment, with one of its endpoints as the center and the length of the segment as the radius, you can make a circle;

[0127] The fourth postulate is that all right angles are equal;

[0128] The fifth postulate is that if two straight lines both intersect a third straight line, and the sum of the interior angles of the same side is less than the sum of the two right angles, then the two straight lines must intersect on this side.

[0129] The five axioms are:

[0130] The first axiom is that quantities of the same magnitude are equal to one another;

[0131] The second axiom is that equal quantities added together equal sums.

[0132] The third axiom is that when equal quantities are subtracted from equal quantities, the difference is still equal.

[0133] The fourth axiom is that objects that can overlap each other are congruent;

[0134] The fifth axiom is that the whole of space is greater than its parts.

[0135] Artificial Intelligence Priori Logic Cognitive Matrix Function A 1 Each element in the second row represents the judgment value of the spatial logic cognitive parameter. Each element is composed of the weighted sum of the judgment values ​​of 10 spatial logic cognitive parameters of the five postulates and five axioms of Euclidean geometry, that is,

[0136] a 2n =η k p k1 +θ k p k2 +ζ k p k3 +ρ k p k4 +σ k p k5 +δ k p k6

[0137] +κ k p k7 +λ k p k8 +ν k p k9 +π k p k10 (3.18)

[0138] This formula is called the artificial intelligence space logic cognition equation, where p k1 、p k2 、p k3 、p k4 、p k5 、p k6 、p k7 、p k8 、p k9 、p k10 are the cognitive judgment values ​​of Euclidean geometry, including five postulates and five axioms, η k ,θ k , k , k , σ k , δ k , κ k , k , ν k , π k They are the weight coefficients of the spatial logic judgment corresponding to each of the five postulates and five axioms in Euclidean geometry. The weight coefficients represent the importance of each spatial logic law judgment.

[0139] Three artificial intelligence time logic cognition equations

[0140] Artificial intelligence time logic cognition is built based on the eight laws of time logic. Artificial intelligence time logic contains eight laws:

[0141] The first law is that time has the attribute of the present, that is, the present tense, which is the time thinking concept of events that are happening at the moment;

[0142] The second law is that time has the attribute of the past, that is, the past tense, which is the time thinking concept of historical events;

[0143] The third law is that time has the attribute of the future, that is, the future tense, which is the time thinking concept of future events;

[0144] The fourth law is that time has a directional attribute. The directional attribute of time includes two types: forward time thinking and reverse time thinking. Forward time thinking is the concept of time running and developing from the past tense to the present tense, and then to the future tense. Reverse time thinking is the concept of time running and developing from the future tense to the present tense, and then to the past tense.

[0145] The fifth law is that time has the property of being static. The static property of time is a cognitive concept that describes the state of events being stopped.

[0146] The sixth law is that time has the property of continuity. The property of continuity of time means that the temporal development of events must be continuous and cannot jump. The property of continuity of time is a direct description of the occurrence of real physical events.

[0147] The seventh law is that time has a linear property. The linear property of time is manifested in that the direction of time is linear. That is, whether it is the forward time running and developing process from the past to the present and then to the future, or the reverse time running and developing process from the future to the present and then to the past, they are all linear developments. The time of occurrence of the same physical event cannot exist in a nonlinear form.

[0148] The eighth law is that time has the property of transmission. The transmission property of time is manifested in that if two different events occur at the same time as a third event, then the time at which these two events occur is also the same. This is called the simultaneity of time. In other words, the simultaneity of time realizes the time transmission process of different events.

[0149] In formula (3.16), the artificial intelligence class prior logic cognitive matrix function A 1 The elements in represent a quasi-a priori logical cognitive event proposition, where the element a 31 a 32 . . . a 3n is the time cognition logic element variable. Artificial intelligence a priori logic cognition matrix function A 1 Each element in the third row represents the judgment value of the time logic cognitive parameter. Each element is composed of the weighted sum of the judgment values ​​of the eight time logic cognitive parameters, that is,

[0150] a 3n =η t p t1 +θ t p t2 +ζ t p t3 +ρ t p t4 +σ t p t5 +δ t p t6

[0151] +κ t p t7 +λ t p t8 (3.19)

[0152] This formula is called the artificial intelligence time logic cognition equation, where p t1 、p t2 、p t3 、pt4 、p t5 、p t6 、p t7 、p t8 are the cognitive judgment values ​​of the eight laws of time logic, η t ,θ t , t , t , σ t , δ t , κ t , t They are the weight coefficients of the time logic judgment corresponding to each of the eight laws of time logic, and the weight coefficients represent the importance proportion of each time logic law judgment.

[0153] 3.3 Mathematical Principles of the Basic Cognitive Model of Artificial Intelligence

[0154] An Artificial Intelligence Appetite Logic Cognitive Equation

[0155] Let us introduce the appetite-like cognitive model in the basic cognitive consciousness framework of artificial intelligence.

[0156] Given an artificial intelligence basic cognitive matrix function A 2 , A 2 It is a 3×n matrix function, called the basic cognitive pixel matrix, which represents the basic cognitive pixels of artificial intelligence and can reflect the basic cognitive accuracy of artificial intelligence. Artificial intelligence basic cognitive matrix function A 2 The elements in represent the judgment value of a basic cognitive event proposition, such as the judgment value of a proposition about appetitive behavior.

[0157] Artificial Intelligence Basic Cognitive Matrix Function A 2 It includes three types of element variables, namely, appetite-like cognitive element variables, risk avoidance cognitive element variables, and sexual desire-like cognitive element variables. Basic cognitive matrix function A 2 The element in b represents a basic cognitive event proposition, 11 b 12 . . . b 1n is the appetite cognitive element variable, b 21 b 22 . . . b 2n To avoid danger, the cognitive element variable of desire, b 31 b 32 . . . b 3n is a class of sexual desire cognitive element variables, which has the following form

[0158]

[0159] Artificial intelligence's appetite-like cognitive consciousness includes the following six aspects of cognitive judgment:

[0160] Cognitive judgment 1: whether the AI’s energy shortage cognitive desire is triggered;

[0161] Cognitive judgment 2: whether the AI’s cognitive desire for energy acquisition is triggered;

[0162] Cognitive judgment three: whether the computer hardware resource configuration of artificial intelligence is insufficient to trigger cognitive desire;

[0163] Cognitive judgment 4: Whether the AI’s computing power resources are insufficient to trigger cognitive desire;

[0164] Cognitive judgment five: whether the cognitive desire is triggered by insufficient optimization of the AI ​​software code;

[0165] Cognitive judgment six: whether the cognitive desire is triggered by insufficient big data resources in the standard knowledge base of artificial intelligence.

[0166] Artificial Intelligence Basic Cognitive Matrix Function A 2 Each element can be represented by a number. The value of this number is used as the basis for judging basic cognitive proposition events and also determines the basic cognitive ability of artificial intelligence. Basic cognitive matrix function A 2 Each element in the first row represents the judgment value of the appetite-like cognitive parameter. Based on the six cognitive judgments of the appetite-like cognitive consciousness of artificial intelligence, it can be seen that each element is composed of the weighted sum of the judgment values ​​of the six appetite-like cognitive parameters, that is,

[0167] b 1n =ε s p s1 +μ s p s2 +ξ s p s3 +τ s p s4 +π s p s5 +θ s p s6 (3.21)

[0168] This formula is called the artificial intelligence appetite cognition equation, where p s1 、p s2 、p s3 、p s4 、p s5 、p s6 are the cognitive judgment values ​​of the six cognitive judgments of appetite cognition of artificial intelligence, ε s , μ s , s , τ s , πs ,θ s They are the weight coefficients of the appetite-like cognitive judgments corresponding to each of the six cognitive judgments of artificial intelligence appetite cognition, and the weight coefficients represent the importance proportion of each appetite-like cognitive judgment.

[0169] 2. Artificial Intelligence Avoids Danger Desire Logical Cognitive Equation

[0170] In the foundation of artificial intelligence cognitive matrix function A 2 In the example, each element represents a basic cognitive event proposition, b 21 b 22 . . . b 2n To avoid danger, there are cognitive element variables. The cognitive awareness of artificial intelligence's desire to avoid danger includes the following six aspects of cognitive judgment:

[0171] Cognitive judgment 1: whether the AI’s cognitive desire to identify dangers and disasters is triggered;

[0172] Cognitive judgment 2: whether the AI's cognitive desire to avoid obstacles is triggered;

[0173] Cognitive judgment three: whether the AI’s cognitive desire to avoid danger is triggered;

[0174] Cognitive judgment 4: whether the AI ​​safety prevention cognitive desire is triggered;

[0175] Cognitive judgment 5: Whether the AI’s health is insufficient and cognitive desire is triggered;

[0176] Cognitive judgment six: whether the cognitive desire for artificial intelligence safety rescue is triggered.

[0177] Artificial Intelligence Basic Cognitive Matrix Function A 2 Each element can be represented by a number. The value of this number is used as the basis for judging basic cognitive proposition events and also determines the basic cognitive ability of artificial intelligence. Basic cognitive matrix function A 2 Each element in the second row represents the judgment value of the cognitive parameter of the desire to avoid danger. Based on the six cognitive judgments of the cognitive awareness of the desire to avoid danger of artificial intelligence, it can be seen that each element is composed of the weighted sum of the judgment values ​​of the six cognitive parameters of the desire to avoid danger, that is,

[0178] b 2n =ε g p g1 +μ g p g2 +ξ g p g3 +τ g p g4 +π g p g5 +θg p g6 (3.22)

[0179] This formula is called the artificial intelligence risk avoidance desire cognitive equation, where p g1 、p g2 、p g3 、p g4 、p g5 、p g6 are the cognitive judgment values ​​of the six cognitive judgments of artificial intelligence's desire to avoid danger, ε g , μ g , g , τ g , π g ,θ g They are the weight coefficients of the cognitive judgment of avoiding danger desire corresponding to each of the six cognitive judgments of artificial intelligence's desire to avoid danger, and the weight coefficients represent the importance proportion of each cognitive judgment of avoiding danger desire.

[0180] Three artificial intelligence sexual desire logic cognitive equations

[0181] In the foundation of artificial intelligence cognitive matrix function A 2 In the example, each element represents a basic cognitive event proposition, b 31 b 32 . . . b 3n is a variable of quasi-sexual desire cognitive element. The quasi-sexual desire cognitive consciousness of artificial intelligence includes the following four aspects of cognitive judgment:

[0182] Cognitive judgment 1: artificial intelligence's sexual desire recognition determines whether the desire is triggered;

[0183] Cognitive judgment 2: whether the cognitive desire for opposite-sex desire after AI gendering is triggered;

[0184] Cognitive judgment three: whether the emotional attachment cognitive desire of the opposite sex after AI gendering is triggered;

[0185] Cognitive judgment four: whether the artificial intelligence’s cognitive desire for self-reproduction is triggered.

[0186] Artificial Intelligence Basic Cognitive Matrix Function A 2 Each element can be represented by a number. The value of this number is used as the basis for judging basic cognitive proposition events and also determines the basic cognitive ability of artificial intelligence. Basic cognitive matrix function A 2 Each element in the third row represents the judgment value of the class sexual desire cognitive parameter. Based on the four cognitive judgments of artificial intelligence class sexual desire cognitive consciousness, it can be seen that each element is composed of the weighted sum of the judgment values ​​of the four class sexual desire cognitive parameters, that is,

[0187] b3n =ε e p e1 +μ e p e2 +ξ e p e3 +τ e p e4 (3.23)

[0188] This formula is called the artificial intelligence sexual desire cognition equation, where p e1 、p e2 、p e3 、p e4 are the cognitive judgment values ​​of the four cognitive judgments of artificial intelligence sexual desire cognition, ε e , μ e , e , τ e They are the weight coefficients of the sexual desire cognitive judgment corresponding to each of the four cognitive judgments of artificial intelligence sexual desire cognition, and the weight coefficients represent the importance proportion of each sexual desire cognitive judgment.

[0189] 3.4 Mathematical Principles of the Social Cognition Model of Artificial Intelligence

[0190] 1. Artificial Intelligence Moral Logic Cognitive Equation

[0191] The moral cognition in the social cognitive consciousness framework of artificial intelligence is based on human moral attributes. The moral cognitive consciousness of artificial intelligence includes the following six aspects of cognitive judgment:

[0192] Cognitive judgment 1: whether the personal moral cognition of artificial intelligence is triggered;

[0193] Cognitive judgment 2: whether the social public moral cognition of artificial intelligence is triggered;

[0194] Cognitive judgment three: whether the moral cognition of the state will of artificial intelligence is triggered;

[0195] Cognitive judgment 4: whether the social organizational moral cognition of artificial intelligence is triggered;

[0196] Cognitive judgment five: whether the family moral cognition of artificial intelligence is triggered;

[0197] Cognitive judgment six: whether the professional ethics cognition of artificial intelligence is triggered.

[0198] Artificial Intelligence Social Cognition Matrix Function A 3 Each element can be represented by a number. The value of this number is used as the basis for judging social cognitive proposition events and also determines the social cognitive ability of artificial intelligence. Social cognitive matrix function A 3Each element in the first row represents the judgment value of the moral cognitive parameter. Based on the six cognitive judgments of the moral cognitive consciousness of artificial intelligence, it can be seen that each element is composed of the weighted sum of the judgment values ​​of the six moral cognitive parameters, that is,

[0199] c 1n =η d p d1 +θ d p d2 +ζ d p d3 +ρ d p d4 +σ d p d5 +δ d p d6 (3.24)

[0200] This formula is called the artificial intelligence moral cognition equation, where p d1 、p d2 、p d3 、p d4 、p d5 、p d6 are the cognitive judgment values ​​of the six cognitive judgments of artificial intelligence moral cognition, η d ,θ d , d , d , σ d ,δ d They are the weight coefficients of the moral cognitive judgments corresponding to each of the six cognitive judgments of artificial intelligence moral cognition. The weight coefficients indicate the importance of each moral cognitive judgment.

[0201] 2. Artificial Intelligence Legal Logic Cognitive Equation

[0202] Legal cognition in the social cognitive awareness framework of artificial intelligence is based on the legal attributes of human beings. The legal cognitive awareness of artificial intelligence includes the following six aspects of cognitive judgment:

[0203] Cognitive judgment 1: whether the legal theory and legal thought logic cognition of artificial intelligence is triggered;

[0204] Cognitive judgment 2: whether the legislative logic cognition of artificial intelligence is triggered;

[0205] Cognitive judgment three: whether the logical cognition of the Chinese legal system of artificial intelligence is triggered;

[0206] Cognitive judgment 4: whether the logical cognition of other national legal systems of artificial intelligence is triggered;

[0207] Cognitive judgment 5: whether the international law logic cognition of artificial intelligence is triggered;

[0208] Cognitive judgment six: whether the logical cognition of the historical legal system of artificial intelligence is triggered.

[0209] Artificial Intelligence Social Cognition Matrix Function A 3 Each element in the second row represents the judgment value of the legal cognitive parameter. Based on the six cognitive judgments of artificial intelligence legal cognitive consciousness, it can be seen that each element is composed of the weighted sum of the six legal cognitive parameter judgment values, that is,

[0210] C 2n =η f p f1 +θ f p f2 +ζ f p f3 +ρ f p f4 +σ f p f5 +δ f p f6 (3.25)

[0211] This formula is called the artificial intelligence legal cognition equation, where p f1 、p f2 、p f3 、p f4 、p f5 、p f6 are the cognitive judgment values ​​of the six cognitive judgments of artificial intelligence legal cognition, η f ,θ f , f , f , σ f , δ f They are the weight coefficients of the legal cognitive judgments corresponding to each of the six cognitive judgments of artificial intelligence legal cognition. The weight coefficients indicate the importance of each legal cognitive judgment.

[0212] 3. Artificial Intelligence Social Ethical Logic Cognitive Equation

[0213] The social ethical cognition in the social cognitive awareness framework of artificial intelligence is based on the social ethical attributes of human beings. The social ethical cognitive awareness of artificial intelligence includes the following seven aspects of cognitive judgment:

[0214] Cognitive judgment 1: whether the natural ethical logic cognition of artificial intelligence is triggered;

[0215] Cognitive judgment 2: whether the social ethical logic cognition of artificial intelligence is triggered;

[0216] Cognitive judgment three: whether the scientific and technological ethics cognition of artificial intelligence is triggered;

[0217] Cognitive judgment 4: whether the legal ethical logic cognition of artificial intelligence is triggered;

[0218] Cognitive judgment five: whether the religious ethical cognition of artificial intelligence is triggered;

[0219] Cognitive judgment six: whether the political ethics cognition of artificial intelligence is triggered;

[0220] Cognitive judgment seven: whether the war ethics cognition of artificial intelligence is triggered.

[0221] Artificial Intelligence Social Cognition Matrix Function A 3 Each element in the third row represents the judgment value of the social ethics cognitive parameter. Based on the seven cognitive judgments of artificial intelligence social ethics cognitive consciousness, it can be seen that each element is composed of the weighted sum of the judgment values ​​of the seven social ethics cognitive parameters, that is,

[0222] C 3n =η l p l1 +θ l p l2 +ζ l p l3 +ρ l p l4 +σ l p l5 +δ l p l6 +κ l p l7 (3.26)

[0223] This formula is called the artificial intelligence social ethics cognition equation, where p l1 、p l2 、p l3 、p l4 、p l5 、p l6 、p l7 are the cognitive judgment values ​​of the seven cognitive judgments of artificial intelligence social ethics cognition, η l ,θ l , l , l , σ l , δ l , κ l They are the weight coefficients of the social ethical cognitive judgments corresponding to each of the seven cognitive judgments of social ethical cognition of artificial intelligence, and the weight coefficients indicate the importance of each social ethical cognitive judgment.

[0224] Four Artificial Intelligence Religious Belief Logical Cognitive Equations

[0225] The religious belief cognition in the social cognitive consciousness framework of artificial intelligence is based on the religious belief attributes of people. The religious belief cognition consciousness of artificial intelligence includes the following seven aspects of cognitive judgment:

[0226] Cognitive judgment 1: whether the religious philosophy and religious thought logic cognition of artificial intelligence is triggered;

[0227] Cognitive judgment 2: whether the Christian logical cognition of artificial intelligence is triggered;

[0228] Cognitive judgment three: whether the Islamic logic cognition of artificial intelligence is triggered;

[0229] Cognitive judgment 4: whether the Buddhist logic cognition of artificial intelligence is triggered;

[0230] Cognitive judgment five: whether the logical cognition of the Chinese religious system of artificial intelligence is triggered;

[0231] Cognitive judgment six: whether the logical cognition of other national religious systems of artificial intelligence is triggered;

[0232] Cognitive judgment seven: whether the natural religious logic cognition of artificial intelligence is triggered.

[0233] Artificial Intelligence Social Cognition Matrix Function A 3 Each element in the fourth row represents the judgment value of the religious belief cognitive parameter. Based on the 7 cognitive judgments of the artificial intelligence religious belief cognitive consciousness, it can be seen that each element is composed of the weighted sum of the judgment values ​​of the 7 religious belief cognitive parameters, that is,

[0234] C 4n =η z p z1 +θ z p z2 +ζ z p z3 +ρ z p z4 +σ z p z5 +δ z p z6 +κ z p z7 (3.27)

[0235] This formula is called the religious belief cognition equation of artificial intelligence, where p z1 、p z2 、p z3 、p z4 、p z5 、p z6 、p z7 are the cognitive judgment values ​​of the seven cognitive judgments of artificial intelligence’s religious belief cognition, η z ,θz , z , z , σ z ,δ z , κ z They are the weight coefficients of the religious belief cognitive judgment corresponding to each of the 7 cognitive judgments of artificial intelligence religious belief cognition, and the weight coefficients indicate the importance proportion of each religious belief cognitive judgment.

[0236] Five Artificial Intelligence Mental and Psychological Logic Cognitive Equations

[0237] The spiritual and psychological cognition in the social cognitive consciousness framework of artificial intelligence is based on the spiritual and psychological attributes of human beings. The spiritual and psychological cognitive consciousness of artificial intelligence includes the following four aspects of cognitive judgment:

[0238] Cognitive judgment 1: whether the psychological structure theory and logical cognition of artificial intelligence are triggered;

[0239] Cognitive judgment 2: whether the mental health logic cognition of artificial intelligence is triggered;

[0240] Cognitive judgment three: whether the mental abnormality and pathological psychological logic cognition of artificial intelligence are triggered;

[0241] Cognitive judgment four: whether the spiritual and psychological treatment logical cognition of artificial intelligence is triggered.

[0242] Artificial Intelligence Social Cognition Matrix Function A 3 Each element in the fifth row represents the judgment value of the mental and psychological cognitive parameters. Based on the four cognitive judgments of mental and psychological cognitive consciousness, it can be seen that each element is composed of the weighted sum of the judgment values ​​of the four mental and psychological cognitive parameters, that is,

[0243] C 5n =η j p j1 +θ j p j2 +ζ j p j3 +ρ j p j4 (3.28)

[0244] This formula is called the mental and psychological cognitive equation of artificial intelligence, where p j1 、p j2 、p j3 、p j4 are the cognitive judgment values ​​of the four cognitive judgments of artificial intelligence mental and psychological cognition, η j ,θ j , j , jThey are the weight coefficients of the mental and psychological cognitive judgments corresponding to each of the four cognitive judgments of artificial intelligence mental and psychological cognition, and the weight coefficients represent the importance proportion of each mental and psychological cognitive judgment.

[0245] 3.5 Mathematical Principles of the Cognitive Model of Artificial Intelligence Knowledge Learning

[0246] 1. Artificial Intelligence Pure Knowledge Learning Cognitive Equation

[0247] The pure knowledge learning cognition in the artificial intelligence knowledge learning cognitive consciousness framework is based on human pure knowledge learning. The pure knowledge learning cognitive consciousness of artificial intelligence includes the following five aspects of cognitive judgment:

[0248] Cognitive judgment 1: whether the pure standard knowledge learning logic cognition of artificial intelligence is triggered;

[0249] Cognitive judgment 2: whether the social norms, standards, knowledge, learning, logic and cognition of artificial intelligence are triggered;

[0250] Cognitive judgment three: whether the social and cultural standard knowledge learning logic cognition of artificial intelligence is triggered;

[0251] Cognitive judgment 4: whether the movement and behavior standard knowledge learning logic cognition of artificial intelligence is triggered;

[0252] Cognitive judgment five: whether the spiritual and psychological cognitive standards, knowledge learning, and logical cognition of artificial intelligence are triggered.

[0253] Artificial Intelligence Pure Knowledge Learning Cognitive Matrix Function A 4 Each element in the first row represents the judgment value of the pure knowledge learning cognitive parameter. Based on the five cognitive judgments of pure knowledge learning cognitive consciousness of artificial intelligence, it can be seen that each element is composed of the weighted sum of the judgment values ​​of the five pure knowledge learning cognitive parameters, that is,

[0254] d 1n =η c p c1 +θ c p c2 +ζ c p c3 +ρ c p c4 +σ c p c5 (3.29)

[0255] This formula is called the pure knowledge learning cognitive equation of artificial intelligence, where p c1 、p c2 、p c3 、p c4 、p c5is the cognitive judgment value of the five cognitive judgments of pure knowledge learning cognition of artificial intelligence, η c ,θ c , c , c , σ c It is the weight coefficient of the pure knowledge learning cognitive judgment corresponding to each of the five cognitive judgments of pure knowledge learning cognition of artificial intelligence. The weight coefficient represents the importance ratio of the pure knowledge learning cognitive judgment.

[0256] 2. Artificial Intelligence Standard Information Knowledge Learning Cognitive Equation

[0257] The standard information knowledge learning cognition in the artificial intelligence knowledge learning cognitive consciousness framework is based on human standard information knowledge learning. The standard information knowledge learning cognitive consciousness of artificial intelligence includes the following two aspects of cognitive judgment:

[0258] Cognitive judgment 1: Whether the immediacy standard of artificial intelligence, information, knowledge, learning, logic and cognition are triggered;

[0259] Cognitive judgment 2: whether the historical standard information knowledge learning logical cognition of artificial intelligence is triggered.

[0260] Artificial Intelligence Knowledge Learning Cognitive Matrix Function A 4 Each element in the second row represents the judgment value of the standard information knowledge learning cognitive parameter. Based on the two cognitive judgments of the standard information knowledge learning cognitive consciousness of artificial intelligence, it can be seen that each element is composed of the weighted sum of the judgment values ​​of the two standard information knowledge learning cognitive parameters, that is,

[0261] d 2n =η x p x1 +θ x p x2 (3.30)

[0262] This formula is called the standard information knowledge learning cognitive equation of artificial intelligence, where p x1 、p x2 is the cognitive judgment value of the two cognitive judgments of artificial intelligence standard information knowledge learning cognition, η x ,θ x It is the weight coefficient of the standard information knowledge learning cognitive judgment corresponding to each of the two cognitive judgments of artificial intelligence standard information knowledge learning cognition. The weight coefficient represents the importance ratio of the standard information knowledge learning cognitive judgment.

[0263] 3. Knowledge Learning Cognitive Equation for Transforming Standard Information of Artificial Intelligence into Pure Knowledge

[0264] The knowledge learning cognition of the transformation of standard information into pure knowledge by artificial intelligence is based on the logic of the transformation of standard information into pure knowledge. The knowledge learning cognition consciousness of the transformation of standard information into pure knowledge by artificial intelligence includes the following two aspects of cognitive judgment:

[0265] Cognitive judgment 1: Whether the time judgment logic cognition of the transformation of standard information of artificial intelligence into pure knowledge is triggered;

[0266] Cognitive judgment 2: Whether the content of the transformation of standard information of artificial intelligence into pure knowledge judges whether the logical cognition is triggered.

[0267] Artificial Intelligence Knowledge Learning Cognitive Matrix Function A 4 Each element in the third row represents the judgment value of the knowledge learning cognitive parameter of the transformation of standard information into pure knowledge. Based on the two cognitive judgments of the knowledge learning cognitive consciousness of the transformation of standard information into pure knowledge in artificial intelligence, it can be seen that each element is composed of the weighted sum of the judgment values ​​of the knowledge learning cognitive parameter of the transformation of standard information into pure knowledge, that is,

[0268] d 3n =η h p h1 +θ h p h2 (3.31)

[0269] This formula is called the knowledge learning cognitive equation for transforming standard information of artificial intelligence into pure knowledge, where p h1 、p h2 is the cognitive judgment value of the two cognitive judgments of knowledge learning cognition for the transformation of artificial intelligence standard information into pure knowledge, η h ,θ h It is the cognitive judgment weight coefficient corresponding to each of the two cognitive judgments of knowledge learning for transforming standard information into pure knowledge of artificial intelligence. The weight coefficient represents the importance ratio of cognitive judgment of knowledge learning for transforming standard information into pure knowledge.

[0270] 3.6 Mathematical Principles of the Cognitive Model of Artificial Intelligence Thought

[0271] 1. Artificial Intelligence Dialectical Logic Cognitive Equation

[0272] The dialectical logic cognition in the cognitive consciousness framework of artificial intelligence is based on human dialectical logic thinking. The dialectical logic cognitive consciousness of artificial intelligence includes the following three aspects of cognitive judgment:

[0273] Cognitive judgment 1: whether the cognition of the law of unity of opposites of artificial intelligence is triggered;

[0274] Cognitive judgment 2: whether the law of mutual transformation between quantitative change and qualitative change of artificial intelligence is triggered;

[0275] Cognitive judgment three: whether the law of negation of negation of artificial intelligence is triggered.

[0276] Artificial Intelligence Thought Cognitive Matrix Function A 5 Each element in the first row represents the judgment value of the dialectical logic cognitive parameter. Based on the three cognitive judgments of the dialectical logic cognitive consciousness of artificial intelligence, it can be seen that each element is composed of the weighted sum of the judgment values ​​of the three dialectical logic cognitive parameters, that is,

[0277] e 1n =η b p b1 +θ b p b2 +ζ b p b3 (3.32)

[0278] This formula is called the artificial intelligence dialectical logic cognitive equation, where p b1 、p b2 、p b3 is the cognitive judgment value of the three cognitive judgments of artificial intelligence dialectical logic cognition, η b ,θ b , b It is the weight coefficient of the dialectical logic cognitive judgment corresponding to each of the three cognitive judgments of artificial intelligence dialectical logic. The weight coefficient indicates the importance ratio of the dialectical logic cognitive judgment.

[0279] II. Artificial Intelligence Systematic Thinking Logic Cognitive Equation

[0280] The systematic thinking cognition in the framework of artificial intelligence thought and cognitive consciousness is based on the system theory. The systematic thinking and cognitive consciousness of artificial intelligence includes the following three aspects of cognitive judgment:

[0281] Cognitive judgment 1: whether the nonlinear systematic thinking cognition of artificial intelligence is triggered;

[0282] Cognitive judgment 2: whether the closed systematic thinking cognition of artificial intelligence that satisfies the entropy increase principle is triggered;

[0283] Cognitive judgment three: whether the self-organizing systematic thinking cognition of artificial intelligence is triggered.

[0284] Artificial Intelligence Thought Cognitive Matrix Function A 5 Each element in the second row represents the judgment value of the systematic thinking cognitive parameter. Based on the three cognitive judgments of systematic thinking cognitive awareness, it can be seen that each element is composed of the weighted sum of the three systematic thinking cognitive parameter judgment values, that is,

[0285] e 2n =η t p t1+θ t p t2 +ζ t p t3 (3.33)

[0286] This formula is called the artificial intelligence systematic thinking cognitive equation, where p t1 、p t2 、p t3 is the cognitive judgment value of the three cognitive judgments of artificial intelligence systematic thinking cognition, η t ,θ t , t It is the weight coefficient of the systematic thinking cognitive judgment corresponding to each of the three cognitive judgments of artificial intelligence systematic thinking. The weight coefficient indicates the importance ratio of the systematic thinking cognitive judgment.

[0287] Three Artificial Intelligence Theory Cognitive Equations

[0288] The ideological theory and logical cognition in the framework of artificial intelligence ideological cognition consciousness is based on human ideological theory cognitive thinking. The ideological theory cognitive consciousness of artificial intelligence includes the following three aspects of cognitive judgment:

[0289] Cognitive judgment 1: whether the ideological theory standard knowledge logic cognition of artificial intelligence is triggered;

[0290] Cognitive judgment 2: Whether the movement and behavior thought theory standard knowledge logic cognition of artificial intelligence is triggered;

[0291] Cognitive judgment three: whether the spiritual and psychological thought theory standard knowledge logic cognition of artificial intelligence is triggered.

[0292] Artificial Intelligence Thought Cognitive Matrix Function A 5 Each element in the third row represents the judgment value of the ideological theory cognitive parameter. Based on the three cognitive judgments of the ideological theory cognitive consciousness of artificial intelligence, it can be seen that each element is composed of the weighted sum of the judgment values ​​of the three ideological theory cognitive parameters, that is,

[0293] e 3n =η y p y1 +θ y p y2 +ζ y p y3 (3.34)

[0294] This formula is called the thought cognition equation of artificial intelligence, where p y1 、p y2 、p y3 is the cognitive judgment value of the three cognitive judgments of artificial intelligence thought theory cognition, η y ,θ y ,y It is the weight coefficient of the ideological and theoretical cognitive judgment corresponding to each of the three cognitive judgments of artificial intelligence ideological and theoretical cognition. The weight coefficient indicates the importance ratio of the ideological and theoretical cognitive judgment.

[0295] Four artificial intelligence aesthetic cognition equations

[0296] The aesthetic cognition in the cognitive consciousness framework of artificial intelligence is based on human aesthetic thinking. The aesthetic cognitive consciousness of artificial intelligence includes the following three aspects of cognitive judgment:

[0297] Cognitive judgment 1: whether the aesthetic cognition of the AI ​​person is triggered;

[0298] Cognitive judgment 2: whether the natural aesthetic cognition of artificial intelligence is triggered;

[0299] Cognitive judgment three: whether the social aesthetic cognition of artificial intelligence is triggered.

[0300] Artificial Intelligence Thought Cognitive Matrix Function A 5 Each element in the fourth row represents the judgment value of the aesthetic cognitive parameter. Based on the three cognitive judgments of the aesthetic cognitive consciousness of artificial intelligence, it can be seen that each element is composed of the weighted sum of the judgment values ​​of the three aesthetic cognitive parameters, that is,

[0301] e 4n =η s p s1 +θ s p s2 +ζ s p s3 (3.35)

[0302] This formula is called the aesthetic cognition equation of artificial intelligence, where p s1 、p s2 、p s3 is the cognitive judgment value of the three cognitive judgments of artificial intelligence aesthetic cognition, η s ,θ s , s It is the weight coefficient of aesthetic cognitive judgment corresponding to each of the three cognitive judgments of artificial intelligence aesthetic cognition. The weight coefficient indicates the importance ratio of aesthetic cognitive judgment.

[0303] 3.7 Mathematical Principles of Artificial Intelligence Empirical Cognitive Model

[0304] 1. Artificial Intelligence Natural Experience Cognition Equation

[0305] The natural experience cognition in the artificial intelligence experience cognition consciousness framework is based on human natural experience thinking. The natural experience cognition consciousness of artificial intelligence includes the following three aspects of cognitive judgment:

[0306] Cognitive judgment 1: whether the appetitive experience cognition of artificial intelligence is triggered;

[0307] Cognitive judgment 2: whether the AI's desire to avoid danger is triggered;

[0308] Cognitive judgment three: whether the artificial intelligence's quasi-sexual experience cognition is triggered.

[0309] Artificial Intelligence Experience Cognition Matrix Function A 6 Each element in the first row represents the judgment value of the natural experience cognitive parameter. Based on the three cognitive judgments of the natural experience cognitive consciousness of artificial intelligence, it can be seen that each element is composed of the weighted sum of the judgment values ​​of the three natural experience cognitive parameters, that is,

[0310] f 1n =μ k p k1 +τ k p k2 +ω k p k3 (3.36)

[0311] This formula is called the natural experience cognition equation of artificial intelligence, where p k1 、p k2 、p k3 is the cognitive judgment value of the three cognitive judgments of artificial intelligence natural experience cognition, μ k , τ k ,ω k It is the weight coefficient of the natural experience cognitive judgment corresponding to each of the three cognitive judgments of artificial intelligence natural experience. The weight coefficient indicates the importance ratio of the natural experience cognitive judgment.

[0312] 2. Artificial Intelligence Social Experience Cognition Equation

[0313] The social experience cognition in the AI ​​experience cognition consciousness framework is based on human social experience thinking. The social experience cognition consciousness of AI includes the following five aspects of cognitive judgment:

[0314] Cognitive judgment 1: whether the moral experience cognition of artificial intelligence is triggered;

[0315] Cognitive judgment 2: whether the legal experience cognition of artificial intelligence is triggered;

[0316] Cognitive judgment three: whether the social ethical experience cognition of artificial intelligence is triggered;

[0317] Cognitive judgment 4: whether the AI’s religious belief experience cognition is triggered;

[0318] Cognitive judgment five: whether the mental and psychological experience cognition of artificial intelligence is triggered.

[0319] Artificial Intelligence Experience Cognition Matrix Function A 6 Each element in the second row represents the judgment value of the social experience cognitive parameter. Based on the five cognitive judgments of artificial intelligence social experience cognitive consciousness, it can be seen that each element is composed of the weighted sum of the judgment values ​​of the five social experience cognitive parameters, that is,

[0320] f 2n =μ p p p1 +τ p p p2 +ω p p p3 +θ p p p4 +ε p p p5 (3.37)

[0321] This formula is called the social experience cognition equation of artificial intelligence, where p p1 、p p2 、p p3 、p p4 、p p5 is the cognitive judgment value of the five cognitive judgments of artificial intelligence social experience cognition, μ p , τ p ,ω p ,θ p , ε p It is the weight coefficient of the social experience cognitive judgment corresponding to each of the five cognitive judgments of artificial intelligence social experience. The weight coefficient indicates the importance ratio of the social experience cognitive judgment.

[0322] 3 Artificial Intelligence Movement and Behavior Experience Cognition Equations

[0323] The movement and behavior experience cognition in the artificial intelligence experience cognition consciousness framework is based on human movement and execution behavior cognition. The movement and execution behavior experience cognition consciousness of artificial intelligence includes the following two aspects of cognitive judgment:

[0324] Cognitive judgment 1: whether the AI's motion experience cognition is triggered;

[0325] Cognitive judgment 2: whether the experiential cognition of the execution behavior of artificial intelligence is triggered.

[0326] Artificial Intelligence Experience Cognition Matrix Function A 6 Each element in the third row represents the judgment value of the movement and execution behavior experience cognitive parameter. Based on the two cognitive judgments of artificial intelligence movement and execution behavior experience cognitive consciousness, it can be seen that each element is composed of the weighted sum of the judgment values ​​of the two movement and execution behavior experience cognitive parameters, that is,

[0327] f3n =μ q p q1 +τ q p q2 (3.38)

[0328] This formula is called the artificial intelligence motion and behavior experience cognition equation, where p q1 、p q2 is the cognitive judgment value of the two cognitive judgments of artificial intelligence movement and execution behavior experience cognition, μ q , τ q is the weight coefficient of the cognitive judgment of movement and execution behavior experience corresponding to each of the two cognitive judgments of artificial intelligence movement and execution behavior experience. The weight coefficient represents the importance ratio of the cognitive judgment of movement and execution behavior experience.

[0329] 3.8 Mathematical Principles of the Cognitive Model of Passive Execution Behavior of Artificial Intelligence

[0330] An artificial intelligence cognitive equation for passive execution behavior based on user needs

[0331] The passive execution behavior cognition based on user needs in the artificial intelligence passive execution behavior cognitive awareness framework is based on the user demand cognitive logic. The passive execution behavior cognitive awareness based on user needs of artificial intelligence includes the following two aspects of cognitive judgment:

[0332] Cognitive judgment 1: whether the passive execution behavior cognition of artificial intelligence based on user software requirements is triggered;

[0333] Cognitive judgment 2: whether the passive execution behavior cognition of artificial intelligence based on user hardware requirements is triggered.

[0334] Artificial intelligence passive execution behavior cognitive matrix function A 7 Each element in the first row represents the judgment value of the cognitive parameter of the passive execution behavior based on user needs. These judgment values ​​are based on two cognitive judgments of the cognitive awareness of the passive execution behavior based on user needs. It can be seen that each element is composed of the weighted sum of the judgment values ​​of the cognitive parameters of the passive execution behavior based on user needs, that is,

[0335] g 1n =μ j p j1 +τ j p j2 (3.39)

[0336] This formula is called the passive execution behavior cognitive equation of artificial intelligence based on user needs, where p j1 、p j2 is the cognitive judgment value of the two cognitive judgments of the passive execution behavior cognition of artificial intelligence based on user needs, μj , τ j is the weight coefficient of the cognitive judgment of passive execution behavior based on user needs corresponding to each of the two cognitive judgments of the passive execution behavior of artificial intelligence based on user needs. The weight coefficient represents the importance ratio of the cognitive judgment of passive execution behavior based on user needs.

[0337] II. Artificial Intelligence Passive Execution Behavior Cognitive Equation Based on Physical Hardware Stimulation

[0338] The passive execution behavior cognition based on physical hardware stimulation in the artificial intelligence passive execution behavior cognitive awareness framework is based on the cognitive logic of physical hardware sensors. The passive execution behavior cognitive awareness based on physical hardware stimulation of artificial intelligence includes the following three aspects of cognitive judgment:

[0339] Cognitive judgment 1: whether the passive execution behavior cognition of artificial intelligence based on physical behavior stimulation is triggered;

[0340] Cognitive judgment 2: whether the passive execution behavior cognition of artificial intelligence based on chemical behavioral stimulation is triggered;

[0341] Cognitive judgment three: whether the passive execution behavior cognition of artificial intelligence based on biological behavioral stimulation is triggered.

[0342] Artificial intelligence passive execution behavior cognitive matrix function A 7 Each element in the second row represents the judgment value of the cognitive parameter of the passive execution behavior based on physical hardware stimulation. These judgment values ​​are based on the three cognitive judgments of the cognitive awareness of the passive execution behavior based on physical hardware stimulation. It can be seen that each element is composed of the weighted sum of the judgment values ​​of the cognitive parameters of the passive execution behavior based on physical hardware stimulation, that is,

[0343] g 2n =μ r p r1 +τ r p r2 +ξ r p r3 (3.40)

[0344] This formula is called the passive execution behavior cognitive equation of artificial intelligence based on physical hardware stimulation, where p r1 、p r2 、p r3 is the cognitive judgment value of the three cognitive judgments of the passive execution behavior cognition of artificial intelligence based on physical hardware stimulation, μ r , τ r , ris the weight coefficient of the cognitive judgment of passive execution behavior based on physical hardware stimulation corresponding to each of the three cognitive judgments of the passive execution behavior of artificial intelligence based on physical hardware stimulation. The weight coefficient represents the importance ratio of the cognitive judgment of passive execution behavior based on physical hardware stimulation.

[0345] 3. Artificial intelligence passive execution behavior cognitive equation based on software stimulation

[0346] The passive execution behavior cognition based on software stimulation in the artificial intelligence passive execution behavior cognitive awareness framework is based on software stimulation logic. The passive execution behavior cognitive awareness based on software stimulation of artificial intelligence includes the following four aspects of cognitive judgment:

[0347] Cognitive judgment 1: whether the software stimulus in the AI ​​execution process triggers the passive execution behavior cognition;

[0348] Cognitive judgment 2: whether the AI ​​software security behavior stimulates the passive execution behavior cognition to be triggered;

[0349] Cognitive judgment three: whether the mental and psychological cognitive behavior of artificial intelligence stimulates passive execution of behavioral cognition;

[0350] Cognitive judgment four: whether the aesthetic cognitive behavior of artificial intelligence stimulates the passive execution of behavioral cognition.

[0351] Artificial intelligence passive execution behavior cognitive matrix function A 7 Each element in the third row represents the judgment value of the cognitive parameter of the passive execution behavior based on software stimulation. These judgment values ​​are based on the four cognitive judgments of the cognitive awareness of the passive execution behavior based on software stimulation. It can be seen that each element is composed of the weighted sum of the judgment values ​​of the cognitive parameters of the passive execution behavior based on software stimulation, that is,

[0352] g 3n =μ c p c1 +τ c p c2 +δ c p c3 +ρ c p c4 (3.41)

[0353] This formula is called the passive execution behavior cognitive equation of artificial intelligence based on software stimulation, where p c1 、p c2 、p c3 、p c4 is the cognitive judgment value of the four cognitive judgments of the passive execution behavior cognition of artificial intelligence based on software stimulation, μ c , τ c ,δ c, c is the weight coefficient of the cognitive judgment of passive execution behavior based on software stimulation corresponding to each of the four cognitive judgments of the passive execution behavior of artificial intelligence based on software stimulation. The weight coefficient represents the importance proportion of the cognitive judgment of passive execution behavior based on software stimulation.

[0354] 3.9 Mathematical Principles of Artificial Intelligence Self-Creative Cognitive Model

[0355] An artificial intelligence representational self-creative cognitive equation

[0356] The representational self-creative cognition in the artificial intelligence self-creative cognitive consciousness framework is based on the representational creative cognitive logic. The representational self-creative cognitive consciousness of artificial intelligence includes the following four aspects of cognitive judgment:

[0357] Cognitive judgment 1: whether the self-creative cognition of artificial intelligence visual representation is triggered;

[0358] Cognitive judgment 2: whether the self-creative cognition of the artificial intelligence voice representation is triggered;

[0359] Cognitive judgment three: whether the self-creative cognition of artificial intelligence touching the representation is triggered;

[0360] Cognitive judgment four: whether the self-creative cognition of artificial intelligence bionic representation is triggered.

[0361] Artificial Intelligence Self-Creative Cognitive Matrix Function A 8 Each element in the first row represents the judgment value of the representational self-creative cognitive parameter. These judgment values ​​are based on the four cognitive judgments of the representational self-creative cognitive consciousness. It can be seen that each element is composed of the weighted sum of the judgment values ​​of the four representational self-creative cognitive parameters, that is,

[0362] h 1n =μ s p s1 +τ s p s2 +π s p s3 +θ s p s4 (3.42)

[0363] This formula is called the artificial intelligence representational self-creative cognitive equation, where p s1 、p s2 、p s3 、p s4 is the cognitive judgment value of the four cognitive judgments of self-creative cognition of artificial intelligence representation, μ s , τ s , π s ,θs is the weight coefficient of the representational self-creativity cognitive judgment corresponding to each of the four cognitive judgments of artificial intelligence representational self-creativity, and the weight coefficient represents the importance ratio of the representational self-creativity cognitive judgment.

[0364] 2. Artificial Intelligence Content Self-Creative Cognitive Equation

[0365] The content-based self-creative cognition in the artificial intelligence self-creative cognitive awareness framework is based on the content-based creative cognitive logic. The content-based self-creative cognitive awareness of artificial intelligence includes the following three aspects of cognitive judgment:

[0366] Cognitive judgment 1: whether the self-creative cognition of AI literary content is triggered;

[0367] Cognitive judgment 2: whether the self-creative cognition of the text content in the artificial intelligence industry is triggered;

[0368] Cognitive judgment three: whether the content of artificial intelligence academic papers triggers self-creative cognition.

[0369] Artificial Intelligence Self-Creative Cognitive Matrix Function A 8 Each element in the second row represents the judgment value of the content-based self-creative cognitive parameter. These judgment values ​​are based on the three cognitive judgments of the content-based self-creative cognitive consciousness. It can be seen that each element is composed of the weighted sum of the judgment values ​​of the three content-based self-creative cognitive parameters, that is,

[0370] h 2n =μ q p q1 +τ q p q2 +π q p q3 (3.43)

[0371] This formula is called the artificial intelligence content self-creative cognitive equation, where p q1 、p q2 、p q3 is the cognitive judgment value of the three cognitive judgments of artificial intelligence content self-creative cognition, μ q , τ q , π q is the weight coefficient of the content self-creativity cognitive judgment corresponding to each of the three cognitive judgments of artificial intelligence content self-creativity. The weight coefficient represents the importance ratio of the content self-creativity cognitive judgment.

[0372] Three Artificial Intelligence Principles Class Self-Creative Cognitive Equations

[0373] The principle-based self-creative cognition in the artificial intelligence self-creative cognitive consciousness framework is based on the theoretical principle creative cognitive logic. The principle-based self-creative cognitive consciousness of artificial intelligence includes the following three aspects of cognitive judgment:

[0374] Cognitive judgment 1: whether artificial intelligence triggers self-creative cognition based on the principles of natural science research;

[0375] Cognitive judgment 2: whether the self-creative cognition of artificial intelligence social science research principles is triggered;

[0376] Cognitive judgment three, whether the self-creative cognition of the research principles of artificial intelligence humanities is triggered.

[0377] Artificial Intelligence Self-Creative Cognitive Matrix Function A 8 Each element in the third row represents the judgment value of the principle-based self-creative cognitive parameter. These judgment values ​​are based on the three cognitive judgments of the principle-based self-creative cognitive consciousness. It can be seen that each element is composed of the weighted sum of the judgment values ​​of the three principle-based self-creative cognitive parameters, that is,

[0378] h 3n =μ z p z1 +τ z p z2 +π z p z3 (3.44)

[0379] This formula is called the self-creative cognitive equation of artificial intelligence principle, where p z1 、p z2 、p z3 is the cognitive judgment value of the three cognitive judgments of self-creative cognition of artificial intelligence principles, μ z , τ z , π z is the weight coefficient of the principle-based self-creativity cognitive judgment corresponding to each of the three cognitive judgments of artificial intelligence principle-based self-creativity. The weight coefficient represents the importance ratio of the principle-based self-creativity cognitive judgment.

[0380] Four Self-Creative Cognitive Equations for Artificial Intelligence Technology Invention

[0381] The self-creative cognition of technological invention in the framework of artificial intelligence self-creative cognitive awareness is based on the cognitive logic of technological invention creativity. The self-creative cognitive awareness of technological invention in artificial intelligence includes the following three aspects of cognitive judgment:

[0382] Cognitive judgment 1: whether the self-creative cognition of artificial intelligence technology invention patents is triggered;

[0383] Cognitive judgment 2: whether the self-creativity cognition of artificial intelligence utility model patents is triggered;

[0384] Cognitive judgment three: whether self-creative cognition in artificial intelligence computer software development and design is triggered.

[0385] Artificial Intelligence Self-Creative Cognitive Matrix Function A 8 Each element in the fourth row represents the judgment value of the self-creative cognitive parameter of the technical invention category. These judgment values ​​are based on the three cognitive judgments of the self-creative cognitive consciousness of the technical invention category. It can be seen that each element is composed of the weighted sum of the judgment values ​​of the three self-creative cognitive parameters of the technical invention category, that is,

[0386] h 4n =μ m p m1 +τ m p m2 +π m p m3 (3.45)

[0387] This formula is called the self-creative cognitive equation for the invention of artificial intelligence technology, where p m1 、p m2 、p m3 The cognitive judgment values ​​of the three cognitive judgments of self-creative cognition of artificial intelligence technology invention, μ m , τ m , π m is the weight coefficient of the cognitive judgment of self-creativity of technical invention corresponding to each of the three cognitive judgments of self-creativity of artificial intelligence technical invention. The weight coefficient represents the importance proportion of the cognitive judgment of self-creativity of technical invention.

[0388] 5. Self-creative cognitive equations for artificial intelligence engineering technology

[0389] The engineering technology self-creative cognition in the artificial intelligence self-creative cognitive consciousness framework is based on the engineering technology creative cognitive logic. The engineering technology self-creative cognitive consciousness of artificial intelligence includes the following four aspects of cognitive judgment:

[0390] Cognitive judgment 1: whether the self-creative cognition of artificial intelligence application of mathematical engineering technology is triggered;

[0391] Cognitive judgment 2: whether the self-creative cognition of artificial intelligence application of physical engineering technology is triggered;

[0392] Cognitive judgment three: whether the self-creative cognition of artificial intelligence chemical engineering is triggered;

[0393] Cognitive judgment four: whether the self-creative cognition of artificial intelligence biology and medical engineering is triggered.

[0394] Artificial Intelligence Self-Creative Cognitive Matrix Function A 8 Each element in the fifth row represents the judgment value of the engineering technology self-creative cognitive parameter. These judgment values ​​are based on the four cognitive judgments of the engineering technology self-creative cognitive consciousness. It can be seen that each element is composed of the weighted sum of the judgment values ​​of the four engineering technology self-creative cognitive parameters, that is,

[0395] h 5n =μ n p n1 +τ n p n2 +π n p n3 +θ n p n4 (3.46)

[0396] This formula is called the self-creative cognitive equation of artificial intelligence engineering technology, where p n1 、p n2 、p n3 、p n4 is the cognitive judgment value of the four cognitive judgments of self-creative cognition in artificial intelligence engineering technology, μ n , τ n , π n ,θ n is the weight coefficient of the engineering technology self-creativity cognitive judgment corresponding to each of the four cognitive judgments of artificial intelligence engineering technology self-creativity. The weight coefficient represents the importance ratio of the engineering technology self-creativity cognitive judgment.

[0397] 3.10 Mathematical Principles of the Complete Cognitive Model of Artificial Intelligence

[0398] An Artificial Intelligence Individualized Attribute Cognition Equation

[0399] The individualized attribute cognition in the complete cognitive consciousness framework of artificial intelligence is based on the individualized attribute cognitive logic. The individualized attribute cognitive consciousness of artificial intelligence includes the following two aspects of cognitive judgment:

[0400] Cognitive judgment 1: whether the natural attribute cognition of individualized artificial intelligence is triggered;

[0401] Cognitive judgment 2: whether the cognition of the social attributes of individualized artificial intelligence is triggered.

[0402] Artificial Intelligence Complete Cognitive Matrix Function A 9Each element in the first row represents the judgment value of the cognitive parameter of the individualized attribute of artificial intelligence. These judgment values ​​are based on two cognitive judgments of the individualized attribute of artificial intelligence. It can be seen that each element is composed of the weighted sum of the judgment values ​​of the cognitive parameters of the individualized attribute of artificial intelligence, that is,

[0403] i 1n =μ w p w1 +τ w p w2 (3.47)

[0404] This formula is called the individualized attribute cognition equation of artificial intelligence, where p w1 、p w2 is the cognitive judgment value of the two cognitive judgments of the individualized attribute cognition of artificial intelligence, μ w , τ w is the weight coefficient of the individualized attribute cognitive judgment corresponding to each of the two cognitive judgments of the individualized attribute of artificial intelligence. The weight coefficient represents the importance ratio of the individualized attribute cognitive judgment.

[0405] 2. Comprehensive cognitive equation of artificial intelligence

[0406] The comprehensive cognition in the framework of complete cognitive consciousness of artificial intelligence is based on the comprehensive cognitive logic of artificial intelligence. The comprehensive cognitive consciousness of artificial intelligence includes the following eight aspects of cognitive judgment:

[0407] Cognitive judgment 1: whether the artificial intelligence-type a priori logical cognition is triggered;

[0408] Cognitive judgment 2: whether the cognition of the natural attributes of artificial intelligence is triggered;

[0409] Cognitive judgment three: whether the social attribute cognition of artificial intelligence is triggered;

[0410] Cognitive judgment 4: whether the AI ​​knowledge learning cognition is triggered;

[0411] Cognitive judgment five: whether the artificial intelligence thought cognition is triggered;

[0412] Cognitive judgment six: whether artificial intelligence experience cognition is triggered;

[0413] Cognitive judgment seven: whether the AI ​​passively executed behavior cognition is triggered;

[0414] Cognitive judgment eight: whether the artificial intelligence’s self-creative cognition is triggered.

[0415] Artificial Intelligence Complete Cognitive Matrix Function A 9Each element in the second row represents the judgment value of the comprehensive cognitive parameter. These judgment values ​​are based on the eight cognitive judgments of the comprehensive cognitive consciousness. It can be seen that each element is composed of the weighted sum of the eight comprehensive cognitive parameter judgment values, that is,

[0416] i 2n =μ t p t1 +τ t p t2 +π t p t3 +θ t p t4 +

[0418] This formula is called the comprehensive cognitive equation of artificial intelligence, where p t1 、p t2 、p t3 、p t4 、p t5 、p t6 、p t7 、p t8 is the cognitive judgment value of the 8 cognitive judgments of comprehensive cognition of artificial intelligence, μ t , τ t , π t ,θ t , t , t , η t It is the weight coefficient of the comprehensive cognitive judgment corresponding to each of the 8 cognitive judgments of comprehensive cognition of artificial intelligence. The weight coefficient indicates the importance ratio of the comprehensive cognitive judgment.

[0419] 4. Artificial Intelligence Cognitive Computing Priorities and Two-Layer Operation Model

[0420] At present, most of the artificial intelligence technology models on the market are based on probability theory, that is, the process of artificial intelligence machines performing probability analysis on input big data and obtaining calculation results, thereby realizing the intelligent work of artificial intelligence machines. The biggest problem with these artificial intelligence technology models is that they rely on the data volume of big data input and big data algorithm analysis. The intelligent calculation of artificial intelligence is completely dependent on the supply of big data samples, and will perform holistic calculations on the supplied big data, which is in a "needle in a haystack" calculation mode, with extremely high requirements for computing speed, great loss of electric energy, and extremely dependent on the number of big data samples.

[0421] At present, the artificial intelligence technology models on the market do not conform to the human cognitive model. Although the current artificial intelligence technology models also imitate the neural network model, that is, the artificial neural network technology model is formed, at present, these technologies only imitate the surface structure of the human brain neural network, and there is a complete lack of any logical framework for human cognition. We know that humans, including other higher organisms with brains, have lived on the earth for millions of years, but there has been no essential energy crisis in the biological world, that is, the advanced cognitive activities of humans and other higher organisms with brains do not consume too much earth energy. Humans and other higher organisms with brains can carry out cognitive thinking processes very smoothly. This shows that the cognitive activities of humans and other higher organisms with brains are not at the cost of mechanical energy consumption, and are not based on the logic of exhausting all the nodes of brain neurons, but must have a more benign operating mechanism for cognitive behavior. And this more benign operating mechanism must be based on a certain framework structure, which enables the human brain to accurately identify and process external things and give cognitive judgments.

[0422] Based on this, we have created an artificial intelligence cognitive consciousness framework technology model by imitating human cognitive thinking. Without having to rely too much on high-loss electricity energy, large-scale data and high-performance computing power, we can realize the advanced cognitive process of artificial intelligence as benignly as possible and complete the large-scale industrialization of artificial intelligence.

[0423] Under the artificial intelligence cognitive consciousness framework technology model, this operation priority pattern has undergone a fundamental change. In other words, the artificial intelligence cognitive consciousness framework is a framework system based on human cognition and has a very strong certainty. This certainty is manifested in the advance setting of the cognitive consciousness framework, rather than the probability analysis and mathematical statistics based on the input big data. Therefore, in the artificial intelligence cognitive consciousness framework technology model, the artificial intelligence cognitive consciousness framework big data setting has the highest level of mathematical operation authority, which is greater than the operation authority of the algorithm analysis of ordinary input big data. The operation authority of the ordinary input big data algorithm must be calculated after the mathematical operation authority set by the artificial intelligence cognitive consciousness framework data, and its operation must also comply with the former's operation rules. On this basis, subsequent algorithm operations are performed to output artificial intelligence cognitive operation results or execute intelligent functional behaviors.

[0424] Therefore, the mathematical logic of the AI ​​cognitive consciousness framework technology model is a two-layer operation logic structure. The first layer is the structural layer that sets the AI ​​cognitive consciousness framework. The second layer is based on the first layer and performs data calculation according to the probability analysis and mathematical statistics model of the input big data. Figure 3 .

[0425] In the two-layer structure model of artificial intelligence cognitive computing, cognitive computing is triggered by the input of cognitive needs. For artificial intelligence machines, cognitive needs input includes two types. One type is based on user demand input; the other type is based on the triggering stimulus of the external environment to the artificial intelligence machine, including the collection of external impact, pressure, temperature, gravity, sound, optics and other stimulus command data by AI sensor equipment.

[0426] After obtaining cognitive needs, artificial intelligence will first process the demand data through the artificial intelligence cognitive awareness framework. The purpose of this process is to achieve effective screening of the artificial intelligence cognitive category, allowing the artificial intelligence cognitive awareness framework to perform targeted calculations on the demand data, so as to achieve thinking and cognition that conforms to human natural attributes, social attributes, etc., rather than performing aimless overall calculations, wasting computing power and data resources, and improving computing efficiency and accuracy.

[0427] The AI ​​cognitive consciousness framework will call the standard knowledge base for the calculation of the required data, and feed back to the AI ​​cognitive consciousness framework through the retrieval of pure knowledge or the retrieval of ideological and theoretical algorithms, forming a cognitive result based on the cognitive consciousness framework. This cognitive result will then be passed to the AI ​​big data probability and mathematical statistics algorithm calculation layer for secondary calculation. This calculation process will call the previous AI big data pre-training database, extract the relevant data features of the pre-training database, and feed back to the big data probability and mathematical statistics algorithm calculation layer, and then combine the initial calculation results of the AI ​​cognitive consciousness framework to perform comprehensive cognitive calculations to obtain the final cognitive calculation output results.

[0428] 5. Cognitive execution process of artificial intelligence cognitive model

[0429] The AI ​​cognitive awareness framework technology model can realize complete AI cognitive capabilities, and the execution process is achieved through a standard cognitive execution process. The cognitive execution process of the AI ​​cognitive awareness framework technology model can be described by the AI ​​cognitive technology cognitive execution flow chart, see Figure 4 The AI ​​cognitive awareness framework technology model includes twelve aspects of cognitive execution processes, as follows:

[0430] (1) Artificial Intelligence Cognitive Execution Process 1: Question Input

[0431] The first cognitive process of the AI ​​cognitive model is proposition input. AI proposition input includes two types: one is the input of cognitive propositions in the form of user needs; the other is cognitive stimulus input based on the external perception behavior of AI. These stimulus inputs are obtained through AI hardware sensors to form stimulus proposition inputs.

[0432] The input proposition has a certain meaning. Whether it is the proposition input of user needs or the cognitive stimulation triggered by external perception behavior, it has a certain meaning for the artificial intelligence cognitive consciousness framework. At the same time, the input proposition also has certain problem attributes. Artificial intelligence will subsequently analyze the meaning and proposition attributes of these input propositions to complete the subsequent cognitive process.

[0433] (2) Artificial Intelligence Cognitive Execution Process 2: Semantic Analysis

[0434] After the proposition is input, artificial intelligence begins to perform semantic analysis on the input proposition.

[0435] For propositions of a textual nature (including text converted from speech or images), artificial intelligence first performs word analysis on the proposition text. The purpose of word analysis is to analyze the language logical structure of the text and prepare for the subsequent understanding of the meaning of the text.

[0436] For the proposition about the nature of cognitive stimulation triggered by external perceptual behavior, artificial intelligence needs to first conduct a category analysis of the perceptual stimulation behavior. The purpose of the analysis is to distinguish the type of perceptual stimulation behavior and to prepare for the subsequent understanding of the meaning of the perceptual stimulation behavior.

[0437] The above semantic analysis and analysis of perceptual stimulation behavior are both superficial content analysis, which does not yet have a deep level of analysis. The deep level of meaning analysis is carried out in the subsequent artificial intelligence cognitive process, and here it refers more to the literal analysis of language structure. The analysis of perceptual stimulation behavior is also carried out in the form of classification. Subsequent cognitive calculations will go deeper into the meaning of the proposition and will be more complicated.

[0438] (3) Artificial Intelligence Cognitive Execution Process 3: Semantic Understanding

[0439] The semantic understanding of artificial intelligence is the corresponding cognitive understanding of the content of the proposition text. After semantic analysis, artificial intelligence begins to understand the semantics of the input proposition. For propositions of textual nature, according to the logical structure of semantic analysis, the human social meaning of this proposition text will be obtained. For propositions of cognitive stimulation nature triggered by external perception behavior, artificial intelligence understands the meaning of the perception stimulation behavior according to the type of perception stimulation behavior, and forms an understanding of the stimulation behavior.

[0440] Therefore, the cognitive logic of artificial intelligence includes two types: one is the recognition of natural attributes, and the other is the understanding of human social language attributes. Here it mainly refers to the propositional semantic understanding of human social attributes, and the understanding of semantic meaning is based on the logical structure of language.

[0441] (4) Artificial Intelligence Cognitive Execution Process 4: Cognitive Decomposition

[0442] After semantic understanding, AI begins cognitive decomposition of the input proposition. AI cognitive decomposition is based on the socialized meaning of semantic understanding, and decomposes the socialized meaning of the proposition into nine major meanings according to the nine-layer structure of the AI ​​cognitive consciousness framework. Each major meaning can be further refined into smaller meanings at a deeper level, and the levels can be infinitely refined. This refined knowledge data structure can be represented by a matrix.

[0443] For proposition knowledge data that goes deep into the secondary meaning, it can be directly represented by a matrix, which is called the artificial intelligence cognitive decomposition matrix. Cognitive decomposition is divided based on the cognitive knowledge structure of the standard knowledge database. The cognitive decomposition of artificial intelligence propositions can be divided into three categories according to type:

[0444] The first category is natural cognitive decomposition, which is the cognitive decomposition of propositions on natural forms, mainly the decomposition of content cognition belonging to the category of natural science knowledge. Natural cognitive decomposition includes cognitive decomposition of knowledge attributes such as physics, astronomy, geography, and life sciences. Natural form knowledge can be decomposed from the first-level meaning into the second-level meaning, and the second-level meaning can be decomposed into the third-level meaning, and so on, forming a cognitive decomposition process of natural forms. The second category is social cognitive decomposition, which is the cognitive decomposition of propositions on social forms, mainly the decomposition of content cognition belonging to the category of social science knowledge. Social cognitive decomposition includes cognitive decomposition of knowledge attributes such as morality, law, social ethics, and religious beliefs. Social form knowledge can be decomposed from the first-level meaning into the second-level meaning, and the second-level meaning can be decomposed into the third-level meaning, and so on, forming a cognitive decomposition process of social forms. The third category is psychological cognitive decomposition. Psychological cognitive decomposition is the cognitive decomposition of propositions of psychological form, mainly the decomposition of content cognition belonging to the category of psychological science knowledge. Psychological cognitive decomposition is the cognitive decomposition of knowledge attributes such as people's psychological state and mental state, and decomposes and defines personality states, normal psychological states, and pathological psychological states such as anxiety attributes, depression attributes, fear attributes, and compulsive attributes. These all belong to the category of cognitive psychological cognitive decomposition. Psychological form knowledge can be decomposed from primary meaning to secondary meaning, and secondary meaning to tertiary meaning, and so on, forming a natural form of cognitive decomposition process.

[0445] (5) Artificial Intelligence Cognitive Execution Process 5: Social Attribute Matching

[0446] After cognitive decomposition, AI begins to match the social attributes of the input propositions. Any individual AI will be given different social attributes. AI social attribute matching is to judge and match the legal, moral, social ethical, religious belief, spiritual and psychological attributes based on the meaning of the input propositions, and use these social attributes as the basis for cognitive judgment and standard knowledge base calls.

[0447] The matching of social attributes of artificial intelligence will involve the matching of guiding ideologies of law, morality, social ethics, religious belief, spirit and psychology. For example, the legal attributes of China based on Marxist philosophy cannot be applied to American law, because the guiding ideology of American law is not based on Marxist philosophy. An artificial intelligence machine that has matched the legal attributes of China cannot fully understand American law, because Chinese law and American law are different. If an artificial intelligence machine wants to recognize American law, it needs to match the legal attributes of the United States in order to fully recognize American law.

[0448] The cognition of social attributes such as morality, social ethics, religious beliefs, spirit and psychology also needs to match the corresponding social attributes in order to achieve the cognition of the social attributes of the input proposition.

[0449] (6) Artificial Intelligence Cognitive Execution Process 6: Personality Matching

[0450] After the cognitive social attributes are matched, AI begins to match the input propositions with personality. AI's personality matching is to achieve the judgment and matching of individual aesthetics and independent personality, and uses personality matching as the basis for cognitive judgment and standard knowledge base invocation.

[0451] Quasi-personality matching is based on personalized cognition, which enables the AI ​​machine to have personalized aesthetics and independent quasi-personality, and matches its own aesthetic attributes and independent quasi-personality attributes as an independent quasi-personality cognition of the proposition. For example, when AI is asked to judge the cognitive proposition of which of two singers sings better, it will be based on quasi-personality matching as the basis for cognitive judgment. In the eyes of the general public, there may be some public cognition about the question of who sings better between singer A and singer B, but for the AI ​​individual, after matching the unique quasi-personality, it will have its own aesthetic judgment. For example, it thinks that singer A sings better. This is its unique aesthetic cognition. This does not make any sense. It is only based on its subjective individual aesthetic judgment and individual quasi-personality judgment. These are all achieved through quasi-personality matching.

[0452] (7) Artificial Intelligence Cognitive Execution Process 7: Cognitive Data Matching

[0453] After the personality matching, AI begins to match the input proposition with cognitive data. AI cognitive data matching is based on the decomposed sub-level socialized meanings, calling the corresponding standard knowledge in the AI ​​standard knowledge base. For socialized meanings that only acquire pure knowledge, pure knowledge is directly called and matched to the decomposed socialized meanings; for socialized meanings that belong to cognitive logical judgments, it is necessary to call ideological theory algorithm knowledge, match it to the decomposed socialized meanings, and perform algorithmic cognitive calculations on the decomposed socialized meanings.

[0454] The matching of cognitive data involves the matching of natural subject knowledge, social subject knowledge and psychological subject knowledge data. After the matching of social attributes and personality attributes, the matching of cognitive data is carried out, so as to achieve accurate calling of cognitive data.

[0455] For example, when artificial intelligence is endowed with Chinese legal attributes, when retrieving cognitive data from the standard knowledge database, it will be the knowledge data of China's legal system that will be retrieved, rather than the knowledge data of the United States' legal system, to serve as the data basis for artificial intelligence's legal cognition. This process achieves cognitive data matching, thereby realizing the output of cognitive results based on the Chinese legal system.

[0456] (8) Artificial Intelligence Cognitive Execution Process 8: Individual Experience Cognitive Judgment

[0457] After the cognitive data is matched, the artificial intelligence begins to make individual experience cognitive judgments on the input propositions. The individual experience cognitive judgment of artificial intelligence is to retrieve individual experience knowledge as the basis for experience cognitive judgment. Individual experience cognitive judgment includes two stages. The first stage is when the artificial intelligence individual is running for the first time, its knowledge data of individual experience cognitive judgment is zero, and the individual experience judgment ability of artificial intelligence can be ignored at this time. The second stage is after a long period of operation and development, the artificial intelligence cognitive machine will form its own individual experience and experience cognitive ability.

[0458] Artificial intelligence's individual experience cognitive judgment is combined with the matching of social attribute and personality attribute cognitive data. Artificial intelligence will conduct a comprehensive evaluation of the judgment of the proposition, form its own personalized cognition, and thus output comprehensive cognitive results.

[0459] (9) Artificial Intelligence Cognitive Execution Process 9: Decomposed Social Meaning Cognitive Analysis

[0460] After individual experience, cognition and judgment, artificial intelligence begins to decompose the input proposition into a socialized cognitive analysis.

[0461] The cognitive analysis of the socialized meaning decomposed by artificial intelligence is based on the previous social attribute matching, personality matching, cognitive data matching and cognitive judgment of individual experience, and then a specific cognitive analysis of the decomposed socialized meaning will be carried out. The cognitive analysis of the socialized meaning decomposed by artificial intelligence includes three cognitive analysis contents, as follows:

[0462] The first kind of cognitive analysis of socialized meaning is the decomposition meaning analysis of natural cognition. The decomposition meaning analysis of natural cognition is the cognitive analysis of the meaning after the natural form is decomposed to form the corresponding cognitive logic; the second kind of cognitive analysis of socialized meaning is the decomposition meaning analysis of social cognition. The decomposition meaning analysis of social cognition is the cognitive analysis of the meaning after the social form is decomposed to form the corresponding cognitive logic; the third kind of cognitive analysis of socialized meaning is the decomposition meaning analysis of psychological cognition. The decomposition meaning analysis of psychological cognition is the cognitive analysis of the meaning after the psychological form of a person is decomposed, based on the previous social attribute matching and personality matching process, and then based on experience judgment, a decomposed psychological cognitive meaning is formed, and then the corresponding cognitive logic is formed.

[0463] (10) Artificial Intelligence Cognitive Execution Process 10: Cognitive Synthesis

[0464] After cognitive analysis of decomposed social implications, artificial intelligence begins cognitive synthesis of the input propositions.

[0465] The cognitive synthesis of artificial intelligence is to synthesize the sub-level socialized meanings that match the cognitive data to form a unified set of cognitive results of socialized meanings (i.e., the cognitive result output of pure knowledge) or an algorithm set (i.e., the cognitive result output of theoretical algorithms).

[0466] Cognitive synthesis is a comprehensive cognitive synthesis after the cognitive analysis of the socialized meaning after the previous decomposition, forming a comprehensive cognitive judgment based on an artificial intelligence machine, that is, forming a comprehensive cognitive synthesis algorithm, or the cognitive result of the socialized meaning, which serves as the basis for subsequent cognitive calculations.

[0467] (11) Artificial Intelligence Cognitive Execution Process XI: Comprehensive Cognitive Computing

[0468] After cognitive synthesis, artificial intelligence begins to perform comprehensive cognitive calculations on the input propositions.

[0469] The comprehensive cognitive computing of artificial intelligence is to conduct comprehensive cognitive computing on the previously synthesized social meanings based on the previously formed unified set of cognitive results or algorithm sets of social meanings, build a comprehensive cognitive computing model, and form comprehensive cognitive computing results.

[0470] (12) Artificial Intelligence Cognitive Execution Process 12: Cognitive Result Output

[0471] After comprehensive cognitive calculation, artificial intelligence begins to output cognitive results for the input propositions.

[0472] The output of cognitive results of artificial intelligence is to form a unified calculation result based on comprehensive cognitive calculation, output the comprehensive calculation result, and complete the output of cognitive results of the input proposition, thereby completing the cognitive execution process of the entire artificial intelligence cognitive technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0473] (1) Figure 1 illustrate

[0474] Figure 1 It mainly describes the hierarchical structure of the artificial intelligence cognitive consciousness framework technology model, which is divided into nine levels. The nine-level structure provides deep cognitive capabilities for artificial intelligence.

[0475] The first layer is the artificial intelligence-type a priori logic cognitive consciousness layer, which is the cognitive logic layer of artificial intelligence and can realize formal logic cognition, spatial logic cognition and temporal logic cognition;

[0476] The second layer is the basic cognitive consciousness layer of artificial intelligence, which is the natural attribute layer of artificial intelligence and can realize appetite cognition, danger avoidance desire cognition and sexual desire cognition;

[0477] The third layer is the AI ​​social attribute cognition consciousness layer, which is the AI ​​social attribute layer and can realize moral cognition, legal cognition, social ethics cognition, religious belief cognition, spiritual and psychological cognition;

[0478] The fourth layer is the AI ​​knowledge learning cognitive consciousness layer, which is the pure knowledge acquisition layer of AI and can realize pure knowledge learning logical cognition;

[0479] The fifth layer is the AI ​​thought cognition consciousness layer, which is the AI ​​cognitive methodology layer, and can realize dialectical logic cognition, systematic thinking cognition, ideological theory cognition and aesthetic cognition;

[0480] The sixth layer is the AI ​​experience cognition consciousness layer, which is the AI ​​experience cognition layer and can realize individual experience logic cognition;

[0481] The seventh layer is the AI ​​passive execution behavior cognitive consciousness layer, which is the AI ​​cognitive execution layer and can passively execute user demand instructions;

[0482] The eighth layer is the AI ​​self-creative cognitive consciousness layer, which is the cognitive execution layer of AI and can realize self-creative cognition;

[0483] The ninth layer is the complete cognitive consciousness layer of artificial intelligence. This layer is the cognitive consciousness layer of complete artificial intelligence, which can realize individualized cognition and comprehensive logical cognition.

[0484] (2) Figure 2 illustrate

[0485] Figure 2 It mainly describes the operating logic model of the artificial intelligence cognitive consciousness framework and realizes the cognitive processing process of artificial intelligence on propositional events.

[0486] When artificial intelligence faces the input of cognitive proposition requirements, the demand data will enter the artificial intelligence cognitive consciousness framework for demand analysis and understanding. The demand analysis and understanding of cognitive propositions will call upon the artificial intelligence knowledge learning cognitive consciousness layer, artificial intelligence thought cognitive consciousness layer, and artificial intelligence experience cognitive consciousness layer in the cognitive consciousness framework to conduct corresponding cognitive analysis.

[0487] In this process, the AI ​​knowledge learning cognitive consciousness layer, AI thought cognitive consciousness layer, and AI experience cognitive consciousness layer will call the AI ​​basic cognitive consciousness layer and AI social attribute cognitive consciousness layer according to the content of the required proposition to conduct cognition of natural attributes and social attributes. At the same time, it will also combine the cognitive logic foundation of the AI ​​priori logic cognitive consciousness layer to conduct cognitive analysis of cognitive propositions.

[0488] After the cognitive needs are understood and analyzed, the artificial intelligence knowledge learning cognitive consciousness layer, the artificial intelligence thought cognitive consciousness layer, and the artificial intelligence experience cognitive consciousness layer will push the demand analysis results to the cognitive computing functional area. The cognitive computing functional area will push them to the artificial intelligence passive execution behavior cognitive consciousness layer, the artificial intelligence self-creative cognitive consciousness layer, or the artificial intelligence complete cognitive consciousness layer according to the specific type of proposition requirements. These three layers will perform specific cognitive calculations.

[0489] After the cognitive computing is completed, the cognitive computing results will be pushed to the cognitive execution functional area, which will execute the cognitive computing results and finally output the cognitive computing results to the outside, thus completing the overall operating logic of the artificial intelligence cognitive consciousness framework.

[0490] (3) Figure 3 illustrate

[0491] Figure 3It mainly describes the two-layer structure model of cognitive computing of the artificial intelligence cognitive consciousness framework, and realizes data computing and cognitive logic based on the cognitive consciousness framework. After the proposition event cognitive demand is input, the artificial intelligence cognitive computing two-layer structure model will first transmit the demand data to the artificial intelligence cognitive consciousness framework. The artificial intelligence cognitive consciousness framework will analyze and cognitively calculate the cognitive demand data according to the nine-layer cognitive structure, that is, determine the framework mode of cognitive computing, and perform framework calculations on cognitive needs by determining natural attributes, social attributes, knowledge learning, thought cognition, experience cognition, passive execution behavior cognition, self-creative cognition, individualized cognition and other modes.

[0492] When AI performs framework calculations, it will call the standard knowledge database according to cognitive needs to obtain the required pure knowledge data or theoretical algorithm data to perform framework calculations for cognitive needs, and push the framework calculation results to the AI ​​big data probability and mathematical statistics algorithm calculation layer for specific secondary cognitive calculations. When the AI ​​big data probability and mathematical statistics algorithm calculation layer performs specific secondary cognitive calculations, it will call the AI ​​big data pre-training database to obtain pre-training feature data to perform specific cognitive calculations for cognitive needs.

[0493] Afterwards, the artificial intelligence big data probability and mathematical statistics algorithm calculation layer will combine the framework calculation results of the artificial intelligence cognitive awareness framework to perform comprehensive cognitive calculations, and output the comprehensive cognitive calculation results, thereby completing the cognitive calculation of the artificial intelligence two-layer structure.

[0494] (4) Figure 4 illustrate

[0495] Figure 4 It mainly describes the overall cognitive execution process of the artificial intelligence cognitive consciousness framework technical model, which includes a total of twelve execution processes, through which the cognition of propositional events is realized.

[0496] Through proposition input, artificial intelligence first performs semantic analysis on proposition events and analyzes the language structure of proposition text; then it performs semantic understanding on proposition text to form the true meaning of proposition text; based on the true meaning of proposition text, it performs cognitive decomposition on proposition text according to natural form cognition, social form cognition and psychological form cognition to refine the meaning of proposition text; then, based on the refined meaning of proposition text, it matches social attributes such as law, morality, social ethics, religious belief, spiritual and psychological attributes for refined propositions to realize the recognition of social attributes of propositions; then, it matches personality-like attributes for refined propositions to realize personality-like and personalized aesthetic cognition of propositions; then, it matches personality-like attributes for refined propositions to realize personality-like and personalized aesthetic cognition of propositions; then, it matches personality-like attributes for refined propositions to realize personality-like and personalized aesthetic cognition of propositions. Match the data required for cognition, and match pure knowledge data or theoretical algorithm knowledge data through the artificial intelligence standard knowledge database to realize the support of big data; then, by retrieving the artificial intelligence individual experience data, combined with social attributes and personality-like attributes, make a comprehensive cognitive judgment on the proposition; then, conduct cognitive analysis on the social meaning of the refined proposition to form the social cognitive meaning of the refined proposition; then, according to the social meaning of the analyzed proposition, synthesize the proposition to form unified cognitive synthesis data; then, perform comprehensive cognitive calculation on the cognitive synthesis data to form a comprehensive cognitive calculation result; finally, output the comprehensive cognitive calculation result to complete the cognitive execution process. DETAILED DESCRIPTION

[0497] The platform construction of the artificial intelligence cognitive awareness framework technical model is built and implemented in the form of cloud computing to realize the construction of the artificial intelligence cognitive awareness framework SaaS cloud platform. The cognitive function service of the artificial intelligence cognitive awareness framework SaaS cloud platform adopts a loosely coupled model. The nine-layer structure of the artificial intelligence cognitive awareness framework is deployed to the SaaS cloud platform in the form of functional components. Each layer of cognitive structure contains several functional components, thus forming the following types of functional components:

[0498] (1) The functional components of the artificial intelligence a priori logic cognitive consciousness layer include: formal logic cognitive component, spatial logic cognitive component and temporal logic cognitive component;

[0499] (2) The functional components of the basic cognitive consciousness layer of artificial intelligence include: appetite-like cognitive components, danger avoidance desire cognitive components, and sexual desire-like cognitive components;

[0500] (3) The functional components of the social attribute cognitive consciousness layer of artificial intelligence include: moral cognitive component, legal cognitive component, social ethics cognitive component, religious belief cognitive component, spiritual and psychological cognitive component;

[0501] (4) The functional components of the AI ​​knowledge learning cognitive consciousness layer include: pure knowledge learning logic cognitive components;

[0502] (5) The functional components of the AI ​​thought cognition consciousness layer include: dialectical logic cognition component, systematic thinking cognition component, ideological theory cognition component, and aesthetic cognition component;

[0503] (6) The functional components of the AI ​​experience cognition consciousness layer include: individual experience logic cognition components;

[0504] (7) The functional components of the AI ​​passive execution behavior cognitive consciousness layer include: passive execution behavior cognitive components;

[0505] (8) The functional components of the AI ​​self-creative cognitive consciousness layer include: self-creative cognitive components;

[0506] (9) The functional components of the complete cognitive consciousness layer of artificial intelligence include: individualized cognitive components and comprehensive logical cognitive components.

[0507] At the same time, the artificial intelligence cognitive awareness framework SaaS cloud platform also includes the construction and implementation of the artificial intelligence standard knowledge base. The artificial intelligence standard knowledge base service is also deployed to the SaaS cloud platform in the form of functional components, thus forming the following types of database functional components:

[0508] (1) Artificial intelligence standard pure knowledge database functional components;

[0509] (2) Artificial intelligence standard information knowledge database functional components;

[0510] (1) Artificial intelligence standard theory algorithm knowledge database functional components.

[0511] Through the implementation and construction of the SaaS cloud platform of the above-mentioned artificial intelligence cognitive awareness framework technology model, the artificial intelligence cognitive awareness framework technology model can realize the cognitive function services of artificial intelligence to the outside world, and provide users with artificial intelligence cognitive logic function services, artificial intelligence natural attribute cognitive function services, artificial intelligence social attribute cognitive function services, artificial intelligence knowledge learning cognitive function services, artificial intelligence thought cognitive function services, artificial intelligence experience cognitive function services, artificial intelligence passive execution behavior cognitive function services, artificial intelligence self-creative cognitive function services, artificial intelligence individualized cognitive function services, artificial intelligence comprehensive logic cognitive function services, artificial intelligence standard pure knowledge database function services, artificial intelligence standard information knowledge database function services, artificial intelligence standard theoretical algorithm knowledge database function services and other advanced cognitive services in the form of personalized functional components.

Claims

1. The technical feature of the artificial intelligence cognitive consciousness framework technology model is a nine-layer cognitive structure designed to imitate the human cognitive structure to realize the comprehensive logical cognition of artificial intelligence. We request to protect the nine-layer cognitive structure of the artificial intelligence cognitive consciousness framework technology model, specifically including the artificial intelligence's quasi-a priori logic cognitive consciousness layer, artificial intelligence's basic cognitive consciousness layer, artificial intelligence's social attribute cognitive consciousness layer, artificial intelligence's knowledge learning cognitive consciousness layer, artificial intelligence's thought cognitive consciousness layer, artificial intelligence's experience cognitive consciousness layer, artificial intelligence's passive execution behavior cognitive consciousness layer, artificial intelligence's self-creative cognitive consciousness layer, and artificial intelligence's complete cognitive consciousness layer.

2. The technical feature of the artificial intelligence cognitive consciousness logic model is that it effectively reflects the core operating mode of the artificial intelligence cognitive consciousness framework model. Through artificial intelligence cognitive function equations and mathematical principles, it can realize the complete cognitive computing logic from input to output of proposition requirements, and realize the cognitive processing process of artificial intelligence on proposition events. It is requested to protect the artificial intelligence cognitive consciousness logic model.

3. The technical feature of the two-layer structure model of artificial intelligence cognitive computing is a two-layer artificial intelligence cognitive operation logic structure. The first layer is the structural layer that sets the artificial intelligence cognitive consciousness framework. The second layer is a layer that performs data calculations based on the probability analysis and mathematical statistics model of the input big data on the basis of the first layer. Through the comprehensive cognitive calculation of the two-layer structure, the output of cognitive calculation results is realized. We request to protect the two-layer structure model of artificial intelligence cognitive computing.

4. The technical feature of the overall cognitive execution process of the artificial intelligence cognitive consciousness framework technology model is that the artificial intelligence cognitive execution process is divided into twelve cognitive execution processes, through which the cognitive calculation of propositional events is realized. It is requested to protect the overall cognitive execution process of the artificial intelligence cognitive consciousness framework technology model, which specifically includes the twelve cognitive execution processes of proposition input, semantic analysis, semantic understanding, cognitive decomposition, social attribute matching, personality matching, cognitive data matching, individual experience cognitive judgment, decomposed social meaning cognitive analysis, cognitive synthesis, comprehensive cognitive calculation, and cognitive result output.

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