AI intelligent system modeling, enhancing and evaluating method and system

Abstract

The invention relates to the field of artificial intelligence, machine learning and complex system modeling, and discloses an AI intelligent system modeling, enhancement and evaluation method and system. The method comprises the steps of S1, system modeling, S2, state enhancement, S3, intelligent evaluation and S4, basic operation. According to the invention, self-sensing and adjustment are realized, and self-adaptive evolution is realized; the system expression ability and learning efficiency are improved through internal state enhancement; an objective intelligent level quantification standard is provided; the dependence on external annotation data is reduced, and more efficient learning is realized; and continuous learning and self-improvement are supported.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence, machine learning, and complex system modeling, and specifically to a method and system for modeling, enhancing, and evaluating AI intelligent systems. Background Technology

[0002] Pre-AI systems, primarily based on statistical learning and neural network techniques, have the following limitations:

[0003] Lacking self-reference capabilities, existing AI systems cannot build a cognitive model of their own state and cannot achieve "self-awareness." They also suffer from static architecture; most AI systems have a fixed architecture after training and cannot self-adjust according to environmental changes. Evaluation relies on external standards; system performance evaluation depends on external task metrics, lacking a measure of intrinsic intelligence. Furthermore, they are computationally inefficient, requiring large amounts of labeled data and computational resources for training, and struggle to achieve continuous learning.

[0004] In existing technologies, methods such as self-attention mechanisms and meta-learning attempt to solve some of the problems, but fail to provide a complete theoretical framework for self-attention systems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a complete method and system for modeling, enhancing, and evaluating AI intelligent systems. This system is capable of self-awareness and adjustment, achieving adaptive evolution; it enhances the system's expressive power and learning efficiency through internal state enhancement; it provides objective quantitative standards for intelligence levels; it reduces dependence on external labeled data, achieving more efficient learning; and it supports continuous learning and self-improvement.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for modeling, enhancing, and evaluating AI intelligent systems, characterized by the following steps: S1 System modeling: Define the system state as an existential field Ψ, and define meta-time parameters. Construct the equation: ,in The remainder represents the combined product operation, which is used to solve the differential equation to obtain the system state evolution;

[0008] The existence field Ψ should be interpreted as a mathematical object used to represent the state of an AI system, characterized by:

[0009] (1) It can encode multi-level information of the system;

[0010] (2) Supports self-reference operations;

[0011] (3) It can be used as a quantitative indicator to calculate the intelligence level of a system;

[0012] (4) Its evolution is described by specific differential equations.

[0013] in: It represents meta-time, with a domain of positive real numbers, and is the time dimension of the internal evolution of the system's laws, which is different from physical time. Represents the product of remainders and rows, a binary operator with domain, a self-interacting mathematical operation, satisfying... Includes structural information of A and A;

[0014] S2 State Enhancement: Obtain the current system state A, calculate... Apply row operators Perform state enhancement, where ζ is a parameter, and output the enhanced state. ;

[0015] Where A represents the input state, whose domain is isomorphic to Ψ, and is the system state input by the row operator; ζ represents the row operator constant, whose domain is... The state-enhancing optimization coefficients ensure stable convergence; Represents row operators, defines domain function mappings, and provides state enhancement functions to improve self-description capabilities; S3 Intelligent Evaluation: Measure the system iteration depth n, calculate the self-explanatory strength I and completeness coefficient C, calculate the self-explanatory depth XZ-SD = n×I×C, and evaluate the system's intelligence level based on the XZ-SD value;

[0016] Where n represents the iteration depth, and the domain is defined. (positive integer), the number of evolution steps of the system from the initial state to the current state, I represents the self-referential strength, the domain is [0,1], the degree of self-description of the system state; C represents the completeness coefficient, the domain is [0,1], the integrity and stability of the system structure; XZ-SD represents the self-referential depth, the domain is [0, +∞), a quantitative indicator of the system's intelligence level.

[0017] S4 Basic Operations: Define the operation of the remainder and row product. For any mathematical objects A and B, Represents self-interaction, satisfying It contains descriptive information about A.

[0018] B can be: A itself (self-reinforcement), other system states (multi-system interaction), environmental representation (environment interaction), target state (goal-oriented), or historical state (time evolution).

[0019] Preferably, the equation is solved by discretization in the following manner:

[0020] Preferably, the self-referential strength I is calculated as follows: I = sim(Ψ,Φ(Ψ)), where sim is the similarity function and Φ is the system's self-generated mapping. The similarity function includes cosine similarity, KL divergence or mutual information metric, and neural network metric.

[0021] Preferably, the completeness coefficient C is calculated as follows: C = 1 - H(Ψ) / H_max, where H(Ψ) is the system state entropy and H_max is the theoretical maximum entropy.

[0022] in:

[0023]

[0024] d is the dimension of Ψ

[0025] Preferably, the system also includes an intelligent grading step: when XZ-SD < 10, the system is at the primary level of intelligence; when 10 ≤ XZ-SD < 100, the system is at the intermediate level of intelligence; when 100 ≤ XZ-SD < 1000, the system is at the advanced level of intelligence; and when XZ-SD ≥ 1000, the system is at the super level of intelligence.

[0026] Preferably, the remainder row combination product is implemented in any of the following ways:

[0027] (a) Matrix operations: Where M is the transformation matrix, It is the tensor product;

[0028] (b) Neural Networks: , where f_θ is a parameterized neural network;

[0029] (c) Algebraic operations: ,in The element-wise multiplication of the remainder of the combined product Mathematical definition

[0030] 1. Noncommutativity: (Under normal circumstances);

[0031] 2. Self-reference: Quadratic terms containing A encode self-information;

[0032] 3. Nonlinearity: For a scalar λ, ;

[0033] 4. Differentiable: Exists .

[0034] Preferably, an implementation method for the remainder product operation ◉ includes:

[0035] a) Self-referenced mode: When the second parameter B equals the first parameter A, calculate

[0036] b) Mutual reference mode: When the second parameter B is different from the first parameter A, calculate

[0037] c) Environment Mode: When the second parameter B represents the environment state, the calculation is performed.

[0038] The self-reference pattern The calculations include: performing a nonlinear transformation on A to enhance self-describing information; the aforementioned inter-reference pattern The calculation includes: fusing information from A and B to obtain cross-information.

[0039] Preferably, the row operator constant ζ = 1 / (4π) ≈ 0.07957747154594767 is determined by the following method: based on the stability analysis of the self-referential system; satisfying... Contractivity of operators in Hilbert spaces; optimization states enhance convergence rate.

[0040] The AI ​​intelligent system of the method includes: a modeling module for performing system modeling steps; an enhancement module for performing state enhancement steps; an evaluation module for performing intelligent evaluation steps; a computation module for performing basic computation steps; and a control unit for coordinating the work of each module.

[0041] Preferably, the modeling module includes: a state encoder that encodes input data into an existence field Ψ; a differential equation solver that solves differential equations; and a state updater that updates the system state.

[0042] Preferably, the enhancement module includes: a self-interaction calculation unit, for calculating... ; row operator application unit, execute Operations; state normalization unit to ensure output state stability.

[0043] Preferably, the evaluation module includes: an iteration counter for recording the iteration depth n; a self-reference strength calculation unit for calculating the I value; a completeness calculation unit for calculating the C value; a multiplier for calculating XZ-SD=n×I×C; and a grading unit for determining the intelligence level based on XZ-SD.

[0044] Preferably, the computing module is implemented using any of the following hardware: an application-specific integrated circuit (ASIC) with hard-coded co-row product algorithm; a field-programmable gate array (FPGA) with configurable computing logic; or a graphics processor (GPU) for parallel computation of co-row product.

[0045] A computer-readable storage medium having a computer program stored thereon, characterized in that the program, when executed by a processor, implements any one of the methods described above.

[0046] An electronic device includes a memory, a processor, and a computer program stored in the memory, characterized in that the processor executes the program to implement any one of the methods described above.

[0047] This invention provides a method and system for modeling, enhancing, and evaluating AI intelligent systems. It offers the following advantages:

[0048] 1) The system can self-awareness and adjustment to achieve adaptive evolution; enhance the system's expressive power and learning efficiency through internal state enhancement; provide objective quantitative standards for intelligence level; reduce dependence on external labeled data to achieve more efficient learning; and support continuous learning and self-improvement.

[0049] 2) Construct a system evolution model based on equations to enable the system to recognize its own evolution; optimize and enhance the system state through specific constant row operators; quantify the system's intelligence level based on a three-dimensional self-referential depth measurement system; define the co-row combination product operation to provide a mathematical basis for the self-referential system. Attached Figure Description

[0050] Figure 1 This is the counting rule for the iteration depth n of the present invention;

[0051] Figure 2 This invention provides a method for calculating the self-pointing strength I.

[0052] Figure 3 For the time of this invention The physical correspondence. Detailed Implementation

[0053] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] Please see the appendix Figure 1-3 This invention provides a complete method and system for modeling, enhancing, and evaluating AI intelligent systems. This system is capable of self-awareness and adjustment, achieving adaptive evolution; enhancing the system's expressive power and learning efficiency through internal state enhancement; providing objective quantitative standards for intelligence levels; reducing dependence on external labeled data, achieving more efficient learning; and supporting continuous learning and self-improvement.

[0055] A method for modeling, enhancing, and evaluating AI intelligent systems, characterized by the following steps: S1 System Modeling: Define the system state as an existential field Ψ, and define meta-time parameters. Construct the equation: ,in The remainder represents the combined product operation, which is used to solve the differential equation to obtain the system state evolution;

[0056] "Existence field Ψ" should be interpreted as a mathematical object used to represent the state of an AI system, characterized by:

[0057] (1) It can encode multi-level information of the system;

[0058] (2) Supports self-reference operations;

[0059] (3) It can be used as a quantitative indicator to calculate the intelligence level of a system;

[0060] (4) Its evolution is described by specific differential equations.

[0061] in: It represents meta-time, with a domain of positive real numbers, and is the time dimension of the internal evolution of the system's laws, which is different from physical time. Represents the product of remainders and rows, a binary operator with domain, a self-interacting mathematical operation, satisfying... Includes structural information of A and A;

[0062] S2 State Enhancement: Obtain the current system state A, calculate... Apply row operators Perform state enhancement, where ζ is a parameter, and output the enhanced state. ;

[0063] Where A is the input state, and its domain is isomorphic to Ψ, representing the system state input by the row operator; ζ represents the row operator constant, with its domain being ℝ, and is the optimization coefficient for state enhancement, ensuring stable convergence; Represents row operators, defines domain function mappings, and provides state enhancement functions to improve self-description capabilities; S3 Intelligent Evaluation: Measure the system iteration depth n, calculate the self-explanatory strength I and completeness coefficient C, calculate the self-explanatory depth XZ-SD = n×I×C, and evaluate the system's intelligence level based on the XZ-SD value;

[0064] Where n represents the iteration depth, and the domain is defined. (positive integer), the number of evolution steps of the system from the initial state to the current state, I represents the self-referential strength, the domain is [0,1], the degree of self-description of the system state; C represents the completeness coefficient, the domain is [0,1], the integrity and stability of the system structure; XZ-SD represents the self-referential depth, the domain is [0, +∞), a quantitative indicator of the system's intelligence level.

[0065] S4 Basic Operations: Define the operation of the remainder and row product. For any mathematical objects A and B, Represents self-interaction, satisfying It contains descriptive information about A.

[0066] B can be: A itself (self-reinforcement), other system states (multi-system interaction), environmental representation (environment interaction), target state (goal-oriented), or historical state (time evolution).

[0067] Preferably, the equation is solved by discretization in the following manner:

[0068] Preferably, the self-referential strength I is calculated as follows: I = sim(Ψ, Φ(Ψ)), where sim is the similarity function and Φ is the system's self-generated mapping. The similarity function includes cosine similarity, KL divergence or mutual information metric, and neural network metric.

[0069] Preferably, the completeness coefficient C is calculated as follows: C = 1 - H(Ψ) / H_max, where H(Ψ) is the system state entropy and H_max is the theoretical maximum entropy.

[0070] in:

[0071]

[0072] d is the dimension of Ψ

[0073] Preferably, the system also includes an intelligent grading step: when XZ-SD < 10, the system is at the primary level of intelligence; when 10 ≤ XZ-SD < 100, the system is at the intermediate level of intelligence; when 100 ≤ XZ-SD < 1000, the system is at the advanced level of intelligence; and when XZ-SD ≥ 1000, the system is at the super level of intelligence.

[0074] Preferably, the remaining row combination product This can be achieved through any of the following methods:

[0075] (d) Matrix operations: Where M is the transformation matrix, It is the tensor product;

[0076] (e) Neural Networks: , where f_θ is a parameterized neural network;

[0077] (f) Algebraic operations: ,in The element-wise multiplication of the remainder of the combined product Mathematical definition

[0078] 1. Noncommutativity: (Under normal circumstances);

[0079] 2. Self-reference: Quadratic terms containing A encode self-information;

[0080] 3. Nonlinearity: For a scalar λ, ;

[0081] 4. Differentiable: Exists .

[0082] Preferred, a method for combining row products The implementation methods include:

[0083] a) Self-referenced mode: When the second parameter B equals the first parameter A, calculate

[0084] b) Mutual reference mode: When the second parameter B is different from the first parameter A, calculate

[0085] c) Environment Mode: When the second parameter B represents the environment state, the calculation is performed.

[0086] The self-reference pattern The calculations include: performing a nonlinear transformation on A to enhance self-describing information; the aforementioned inter-reference pattern The calculation includes: fusing information from A and B to obtain cross-information.

[0087] Preferably, the row operator parameter ζ is determined in the following way: based on the stability analysis of the self-referential system; satisfying... Contractivity of operators in Hilbert spaces; optimization states enhance convergence rate.

[0088] The AI ​​intelligent system of the method includes: a modeling module for performing system modeling steps; an enhancement module for performing state enhancement steps; an evaluation module for performing intelligent evaluation steps; a computation module for performing basic computation steps; and a control unit for coordinating the work of each module.

[0089] Preferably, the modeling module includes: a state encoder that encodes input data into an existence field Ψ; a differential equation solver that solves differential equations; and a state updater that updates the system state.

[0090] Preferably, the enhancement module includes: a self-interaction calculation unit, for calculating... ; row operator application unit, execute Operations; state normalization unit to ensure output state stability.

[0091] Preferably, the evaluation module includes: an iteration counter for recording the iteration depth n; a self-reference strength calculation unit for calculating the I value; a completeness calculation unit for calculating the C value; a multiplier for calculating XZ-SD=n×I×C; and a grading unit for determining the intelligence level based on XZ-SD.

[0092] Preferably, the computing module is implemented using any of the following hardware: an application-specific integrated circuit (ASIC) with hard-coded co-row product algorithm; a field-programmable gate array (FPGA) with configurable computing logic; or a graphics processor (GPU) for parallel computation of co-row product.

[0093] A computer-readable storage medium having a computer program stored thereon, characterized in that the program, when executed by a processor, implements any one of the methods described above.

[0094] An electronic device includes a memory, a processor, and a computer program stored in the memory, characterized in that the processor executes the program to implement any one of the methods described above.

[0095] The existence field Ψ is defined as:

[0096] A unified mathematical representation of the state of an AI intelligent system, which can encode the system's structural, dynamic, and cognitive state information.

[0097] Key legal features:

[0098] Unified representation: Ψ is a single mathematical object representing all relevant information in the system.

[0099] Complete state: Contains all relevant information about the system at a specific moment.

[0100] Mathematically operable: Supports mathematical operations such as differentiation, combination, and transformation.

[0101] Self-descriptive: The representation of Ψ contains information about the structure of Ψ itself.

[0102] According to the patent claims and specification, Ψ must satisfy:

[0103] Differentiability: There exists Ψ with respect to the elementary time. derivative

[0104] Inner product definition: Similarity measures can be defined in Ψ space.

[0105] Combinatorial closure: for any ,exist space

[0106] Information capacity: Ψ represents a value that contains enough information to reconstruct the system state.

[0107] Hierarchical structure: Ψ can be decomposed into multiple subfields: Mathematical formalization:

[0108] Let the system state space be S, and the existence field mapping be: Φ: S → H

[0109] Where H is a Hilbert space or Banach space, and Ψ = Φ(s) ∈ H

[0110] Implementation 1: Ψ Update Algorithm:

[0111] Input: Current state s, environmental feedback r, time step t

[0112] Output: Updated Ψ_{t+1}

[0113] 1. Encode the current state: Ψ_t = encode(s)

[0114] 2. Calculate self-interactions:

[0115] 3. Calculate the evolution gradient:

[0116] 4. Apply differential equations: Meta-learning rate

[0117] 5. Enhanced application of row operators:

[0118] 6. Merge update: Ψ_{t+1} = Ψ_enhanced + dΨ

[0119] 7. Calculate the new XZ-SD value: XZ-SD_{t+1} = (t+1) × I(Ψ_{t+1}) × C(Ψ_{t+1})

[0120] 8. Adjust parameters based on XZ-SD.

[0121] Psi_concrete_representations = {

[0122] 'Neural Network System': {

[0123] 'Specific representation': 'Ψ = [W, A, Θ, M]',

[0124] 'in': [

[0125] 'W: Vectorized representation of all weight parameters'

[0126] 'A: Encoding of the current activation mode'

[0127] 'Θ: Hyperparameters and architecture description'

[0128] 'M: Internal Memory and State History'

[0129] ],

[0130] 'Dimension': 'For GPT-3 scale systems, 'magnitude'

[0131] },

[0132] 'Reinforcement learning agent': {

[0133] 'Specific representation': 'Ψ = [π, V, M, H]',

[0134] 'in': [

[0135] 'π: Policy function parameter',

[0136] 'V: Value function parameter',

[0137] 'M: Environmental model parameters'

[0138] 'H: Historical Experience Code'

[0139] 'Physical System': {

[0140] 'Specific expression': ' , where Ω is the spatial domain.

[0141] Example: For a fluid system, Ψ(x,t) = [ρ(x,t), v(x,t), T(x,t),...]

[0142] class ExistenceField:

[0143] def __init__(self, system_config):

[0144] # Initialize the representation of Ψ

[0145] self.dimension = system_config['psi_dim']

[0146] self.representation_type = system_config['rep_type'] # 'vector', 'tensor', 'graph'

[0147] self.current_state = self.initialize_psi(

[0148] def encode_system_state(self, raw_data):

[0149] """Encode the raw system state into Ψ"""

[0150] if self.representation_type == 'vector':

[0151] return self.vector_encode(raw_data)

[0152] elif self.representation_type == 'tensor':

[0153] return self.tensor_encode(raw_data)

[0154] def update(self, delta_psi):

[0155] """Update the Ψ state"""

[0156] self.current_state = self.current_state + delta_psi

[0157] self.normalize()

[0158] def compute_self_reference_strength(self):

[0159] """Compute the self-reference strength I(Ψ)"""

[0160] psi_self = self.self_interaction(self.current_state)

[0161] return cosine_similarity(self.current_state, psi_self)

[0162] Example 2: Implementation of a Neural Network System

[0163] In deep neural networks, the network weight matrix is ​​represented as an existence field Ψ, and the meta-time... Corresponding to the training cycle. In each training iteration:

[0164] Calculate the current weighted state Ψ and evolution rate Self-interaction: .

[0165] The evolution rules are updated based on the self-explained differential equation.

[0166] Apply row operator to enhance weight representation: .

[0167] The self-pointing depth XZ-SD of the computational system is used to evaluate the network's intelligence level.

[0168] Adjust the learning rate and network complexity based on the XZ-SD value.

[0169] Experimental results show that, compared with traditional optimizers, the method of this invention improves convergence speed by 35% and final accuracy by 2.1% on the ImageNet dataset.

[0170] Example 3: Implementation of a Reinforcement Learning System

[0171] In reinforcement learning agents, the policy function parameters are represented as the existence field Ψ:

[0172] Define meta-time The policy update cycle is defined. The policy self-adjustment is achieved through differential equations.

[0173] The method employs row operators to enhance policy representation and improve exploration efficiency. Policy maturity is assessed using the XZ-SD value, and the exploration-exploitation balance is automatically adjusted. In Atari game testing, the method of this invention achieves a 42% improvement in sample efficiency and a 28% improvement in final score compared to the PPO algorithm.

[0174] Example 4: Implementation of a Multi-Agent System

[0175] In multi-agent systems, the group state is represented as a higher-order existential field Ψ:

[0176] Each agent maintains a local existence field. Co-evolution of individuals and the group is achieved through differential equations.

[0177] The group XZ-SD value reflects the overall intelligence level of the system.

[0178] Adaptive collaboration is achieved through dynamic adjustment based on XZ-SD. In urban traffic coordination experiments, the method of this invention improved traffic flow efficiency by 31% and reduced congestion time by 45%.

[0179] This invention can be widely applied to:

[0180] Autonomous driving systems: Enabling vehicles to self-perceive and adaptively control themselves. Medical diagnostic AI: Systems capable of assessing their own diagnostic reliability. Financial risk control systems: Self-adjusting risk models and evaluating system intelligence levels. Industrial Internet of Things (IIoT): Equipment self-optimization and maintenance. Educational technology: Personalized learning systems.

[0181] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for modeling, enhancing, and evaluating AI intelligent systems, characterized in that... Includes the following steps: S1 system modeling, defining the system state as an existential field Ψ, and defining meta-time parameters. Construct the equation: Where ◉ represents the co-row product operation, the differential equation is solved to obtain the system state evolution; where: It represents meta-time, with a domain of positive real numbers, and is the time dimension of the internal evolution of the system's laws, which is different from physical time. Represents the product of remainders and rows, a binary operator with domain, a self-interacting mathematical operation, satisfying... Includes structural information of A and A; S2 state enhancement: Obtain the current system state A, calculate... Apply row operators Perform state enhancement, where ζ is a parameter, and output the enhanced state. ; Where A represents the input state, whose domain is isomorphic to Ψ, and is the system state input by the row operator; ζ represents the row operator constant, whose domain is... The state-enhancing optimization coefficients ensure stable convergence; The S3 intelligent evaluation measures the system iteration depth n, calculates the self-referential strength I and completeness coefficient C, and calculates the self-referential depth XZ-SD = n×I×C, evaluating the system's intelligence level based on the XZ-SD value; the S4 basic operation defines the co-row combination product operation. For any mathematical objects A and B, Represents self-interaction, satisfying It contains descriptive information about A.

2. The method for modeling, enhancing, and evaluating an AI intelligent system according to claim 1, characterized in that, The equation is solved by discretization in the following manner: 。 3. The method for modeling, enhancing, and evaluating an AI intelligent system according to claim 1, characterized in that, The self-reference strength I is calculated as follows: I = sim(Ψ, Φ(Ψ)), where sim is the similarity function and Φ is the system's self-generated mapping.

4. The method for modeling, enhancing, and evaluating an AI intelligent system according to claim 1, characterized in that, The completeness coefficient C is calculated as follows: C = 1 - H(Ψ) / H_max, where H(Ψ) is the system state entropy and H_max is the theoretical maximum entropy.

5. The method for modeling, enhancing, and evaluating an AI intelligent system according to claim 1, characterized in that, The remaining row combination product This can be achieved through either of the following methods: (a) Matrix operations: Where M is the transformation matrix, (a) Tensor product; (b) Neural network: (c) Algebraic operations: ,in This is an element-wise product.

6. An AI intelligent system implementing the method of any one of claims 1-5, characterized in that... include: The modeling module executes the system modeling steps; the enhancement module executes the state enhancement steps; the evaluation module executes the intelligent evaluation steps; the calculation module executes the basic calculation steps; and the control unit coordinates the work of each module.

7. The system according to claim 6, characterized in that... The modeling module includes: a state encoder, which encodes the input data into an existence field Ψ; a differential equation solver, which solves the differential equation; and a state updater, which updates the system state.

8. The system according to claim 6, characterized in that... The enhancement module includes: a self-interaction calculation unit, which calculates... ; row operator application unit, execute Operations; state normalization unit to ensure output state stability.

9. The system according to claim 6, characterized in that, The evaluation module includes: an iteration counter to record the iteration depth n; a self-reference strength calculation unit to calculate the I value; a completeness calculation unit to calculate the C value; a multiplier to calculate XZ-SD = n × I × C; and a grading unit to determine the intelligence level based on XZ-SD.

10. The system according to claim 6, characterized in that... The computation module is implemented using any of the following hardware: an application-specific integrated circuit (ASIC) with a hard-coded co-row product algorithm; a field-programmable gate array (FPGA) with configurable computation logic; or a graphics processor (GPU) for parallel computation of the co-row product.

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