Website GEO Readiness Quantitative Diagnostic Methods and Systems

Abstract

This invention discloses a method and system for quantitative diagnosis of website GEO readiness. The method includes the following steps: S1: collecting raw indicator data for multiple evaluation dimensions; S2: mapping the raw indicator data of each dimension to standard score intervals; S3: calculating the subjective weight vector and objective weight vector of each dimension to obtain the adaptive weight vector of each dimension; S4: constructing a directed acyclic graph G of causal dependencies between each dimension and calculating the dependency decay factor of each dimension; S5: weighted summation to obtain a comprehensive score, and classifying each dimension into different priorities. This invention provides multi-dimensional quantitative diagnosis of the readiness of a website in generative AI search engines, overcoming the shortcomings of existing technologies such as single evaluation dimensions, linear scoring methods, assumptions of independence between dimensions, fixed weights, and lack of predictive ability.

Description

Technical Field

[0001] This invention relates to the field of search engine optimization technology, and in particular to a quantitative diagnostic method and system for website GEO readiness. Background Technology

[0002] With the rapid development of Large Language Model (LLM) technology, generative AI search engines, represented by ChatGPT, Google Gemini, and PerplexityAI, are profoundly changing how users obtain information. Unlike traditional search engines that return lists of links, generative search engines directly generate comprehensive answers and cite relevant web pages as information sources within those answers. This paradigm shift has given rise to the emerging field of Generative Engine Optimization (GEO). The industry also refers to this field as AIO (Artificial Intelligence Optimization), LLMO (Large Language Model Optimization), AEO (Answer Engine Optimization), GAIO (Generative AI Optimization), or SGE (Search Generative Experience) optimization; this invention uses the term GEO uniformly.

[0003] GEO differs fundamentally from traditional SEO in its optimization goals, evaluation dimensions, and technical methods. Traditional SEO evaluation tools (such as Ahrefs Domain Rating, Moz Domain Authority, and SEMrush Authority Score) primarily quantify scores based on dimensions such as the number of backlinks, keyword coverage, and page load speed, which cannot effectively reflect a website's performance in the AI ​​search ecosystem.

[0004] For example, prior art 1: Google PageRank patent (publication number US6285999B1) is based on a single-dimensional scoring of the link graph, which does not involve multi-dimensional nonlinear mapping and causal dependency modeling.

[0005] Existing technology 2: Google's website quality score patent (publication number US8682892B1) evaluates website quality based on user interaction metrics, but does not include GEO-specific dimensions and does not establish causal dependencies between dimensions.

[0006] Existing technology 3: SEMrush Authority Score (non-patented) uses simple linear weighting with fixed weights and no industry-adaptive mechanism. Its AI Visibility metric provides only a single value.

[0007] Existing technology 4: A website rating system patent (publication number WO2019051133A1, Siteimprove) provides multi-dimensional website quality ratings (SEO + accessibility + quality assurance), employing a hierarchical weighted architecture (including Boolean and floating-point sub-scores) and server-side repair suggestions. However, this patent uses linear fixed weights, lacks DAG (Directed Acyclic Graph) causal decay, lacks non-linear dimension-specific function families, lacks GEO-specific dimensions (llms.txt, knowledge graph, EEAT, etc.), lacks gating dimension mechanisms, and lacks GMAO operators.

[0008] In particular, existing technologies fail to answer the following key questions: "If blog content is improved, which downstream dimensions will automatically benefit? By what extent?" This predictive ability of causal cascades between dimensions is completely absent in all existing SEO / GEO tools because they treat each dimension as an independent variable. In practice, a lack of blog content can lead to a vicious cycle, resulting in the inability to establish EEAT (Experience, Expertise, Authoritativeness, Trustworthiness) authority, difficulty in naturally acquiring backlinks, and the inability to identify knowledge graphs. This invention, through a causal dependency graph, achieves for the first time a quantitative modeling and prediction of this cascading effect.

[0009] Furthermore, existing multi-criteria decision aggregation operators (weighted sum, OWA, TOPSIS, etc.) all assume that the dimensions are independent of each other, and there are no mathematical tools to introduce graph topology into aggregation operations. Summary of the Invention

[0010] The technical problem to be solved by the embodiments of the present invention is to provide a quantitative diagnostic method and system for website GEO readiness, so as to perform multi-dimensional quantitative diagnosis of the readiness of a website being cited and recommended in a generative AI search engine, and overcome the shortcomings of the prior art, such as single evaluation dimension, linear scoring method, independent assumption between dimensions, fixed weights, and lack of predictive ability.

[0011] To address the aforementioned technical problems, this invention proposes a quantitative diagnostic method for website GEO readiness, comprising the following steps: S1: Collect raw indicator data from multiple evaluation dimensions from the target website and related platforms; S2: Map the raw indicator data of each dimension to the standard score interval [0,100] to obtain the standard score of each dimension; S3: Calculate the subjective weight vector and objective weight vector for each dimension, linearly fuse the subjective weight vector and objective weight vector, superimpose the enterprise stage adjustment factor and normalize it to obtain the adaptive weight vector for each dimension. S4: Construct a causal dependency directed acyclic graph G between each dimension, and calculate the dependency decay factor for each dimension, wherein the dependency decay factor is the product of the decay contributions of the predecessor dimension. S5: The standard scores, adaptive weights, and dependency decay factors of each dimension are weighted and summed to obtain a comprehensive score. Based on the target gap rate, weight amplification factor, and causal transmission degree, each dimension is automatically classified into different priorities.

[0012] Accordingly, embodiments of the present invention also provide a website GEO readiness quantification diagnostic system, comprising: Data acquisition module: Collects raw indicator data from multiple evaluation dimensions from the target website and related platforms; Standard score calculation module: Maps the raw indicator data of each dimension to the standard score interval [0,100] to obtain the standard score of each dimension; Adaptive weight calculation module: Calculates the subjective weight vector and objective weight vector for each dimension, linearly merges the subjective weight vector and objective weight vector, superimposes the enterprise stage adjustment factor and normalizes it to obtain the adaptive weight vector for each dimension. Decay factor calculation module: Constructs a causal dependency directed acyclic graph G for each dimension, calculates the dependency decay factor for each dimension, and the dependency decay factor is the product of the decay contributions of the predecessor dimension. The quantitative diagnostic module calculates a comprehensive score by weighting and summing the standard scores, adaptive weights, and dependency decay factors of each dimension, and automatically classifies each dimension into different priorities based on the target gap rate, weight amplification factor, and causal transmission degree.

[0013] The beneficial effects of this invention are as follows: (1) This invention establishes for the first time a quantitative diagnostic system for website GEO readiness for generative AI search engines, and for the first time incorporates the unique dimensions of AI engines into the evaluation framework, filling the gap in the evaluation methodology of the GEO field.

[0014] (2) This invention designs a family of nonlinear functions for each dimension and proposes a new mathematical tool called Graph Modulation Aggregation Operator (GMAO). It introduces graph topology into multi-criteria aggregation operations and has provable mathematical properties such as monotonicity, topologically dependent score ceiling, cascade repair synergy effect and exponential defect propagation. It is a generalized extension of existing aggregation operators such as weighted sum / OWA.

[0015] (3) Compared with linear scoring, the nonlinear scoring of the present invention improves the prediction accuracy (AUC) of the actual AI citation results by 11.1%.

[0016] (4) This invention pioneers a dimensional causal dependency decay model based on directed acyclic graphs (DAGs), enabling the automatic discovery of "hidden fatal weaknesses." In the embodiment, the original score of the EEAT dimension is 38 points, which would contribute 38×w²=5.7 points under the traditional independent weighting method; however, under the DAG decay of this invention, the effective contribution of EEAT is only 38×0.330×w²=1.9 points, a decrease of 67%. This cascading decay reveals the hidden vulnerability systematically missed by existing methods, an unexpected technical effect that cannot be detected by any existing tool.

[0017] (5) DAG attenuation produces a qualitative shift in the diagnostic conclusion: For the same brand and the same raw data, EEAT was diagnosed as P2 (low priority, not processed temporarily) under the traditional independent scoring method, but was re-diagnosed as P0 (fatal weakness, must be addressed immediately) under the DAG attenuation method of this invention. This is not a quantitative change in scoring accuracy, but a qualitative change in the diagnostic conclusion—from "no problem" to "a hidden fatal problem exists." This qualitative shift is an unexpected technical effect that existing technologies cannot achieve.

[0018] (6) The industry-adaptive weighting mechanism of the present invention enables the model to achieve Cronbach's α of 0.82, 0.79 and 0.84 respectively in the three industries of DTC / B2B / SaaS.

[0019] (7) The AI ​​citation probability prediction model of the present invention has an AUC of 0.823, which is significantly better than the baseline model (AUC=0.641).

[0020] (8) This invention has wide industrial applicability: it can be directly used for GEO health checks of e-commerce independent websites, multi-brand competitive benchmark analysis, GEO optimization ROI tracking, and AI visibility function module embedding of SEO SaaS platforms, and has complete scalability from single-brand diagnosis to platform-level batch deployment.

[0021] (9) In terms of systems engineering, the present invention provides a complete technical implementation solution, including a three-stage data acquisition pipeline (web crawling, third-party API calls, llms.txt detection), a task queue parallel computing pipeline, a RESTful API output interface, and multiple deployment architectures, to ensure that the method of the present invention can be fully implemented in actual computer systems.

[0022] (10) In terms of engineering efficiency, the computational complexity of the GMAO operator of this invention is only O(N+E), which reduces the training data requirement by more than 90% compared with the neural network scheme (calibration can be completed with 24 brands, while the neural network requires thousands of samples); when the parameter perturbation is ±20%, the comprehensive score fluctuation is only ±4.2 points, and the system stability is significantly better than the black box model; the scoring results are fully interpretable, and the score, decay factor, priority classification and causal attribution (i.e., the generation of causal explanation chain, the directed sequence of backtracking from the P0 dimension to the root cause dimension along the DAG) of each dimension can be traced. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the website GEO readiness quantification diagnostic method according to an embodiment of the present invention.

[0024] Figure 2 This is a comparison chart of the nine-dimensional composite scoring function curves of an embodiment of the present invention.

[0025] Figure 3 This is a causal dependency directed acyclic graph (9 nodes and 10 edges) according to an embodiment of the present invention.

[0026] Figure 4 This is a comparison chart of the nine-dimensional radar scores in embodiments 1-3 of the present invention.

[0027] Figure 5 This is a schematic diagram of the website GEO readiness quantification diagnostic system according to an embodiment of the present invention. Detailed Implementation

[0028] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0029] In this embodiment of the invention, directional indicators (such as up, down, left, right, front, back, etc.) are only used to explain the relative positional relationship and movement of each component in a specific posture (as shown in the figure). If the specific posture changes, the directional indicator will also change accordingly.

[0030] Furthermore, in this invention, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features.

[0031] Please refer to Figure 1 The website GEO readiness quantification diagnostic method of this invention includes the following steps S1 to S5.

[0032] Step S1, Multidimensional Raw Data Collection: Collect raw indicator data for N evaluation dimensions (N≥3) from the target website and related platforms. The N dimensions cover at least two of the three categories: website technical accessibility, content asset quality, and external trust signals.

[0033] Step S2, Intra-dimensional Nonlinear Scoring Mapping: The original index data of each dimension are mapped to the standard score interval [0,100] through the composite scoring function specific to that dimension to obtain the standard score of each dimension; different function families are used for different dimensions.

[0034] Step S3, subjective and objective dual-drive adaptive weight calculation: The subjective and objective weight vectors of each dimension are calculated using the subjective and objective dual-drive weight fusion method. The subjective and objective weight vectors are linearly fused, and the enterprise stage adjustment factor is superimposed and normalized to obtain the adaptive weight vectors of each dimension.

[0035] Step S4, Calculation of causal dependency decay between dimensions: Construct a directed acyclic graph G of causal dependencies between N dimensions (please refer to...). Figure 3 ), calculate the dependency decay factor D for each dimension. i (G), where the dependent decay factor is the product of the decay contributions of the predecessor dimension. When Pred(i) = At that time, D i (G):=1 (empty product convention).

[0036] Step S5: The standard scores, adaptive weights, and dependency decay factors of each dimension are weighted and summed to obtain a comprehensive score. Based on the target gap rate, weight amplification factor, and causal transmission degree, each dimension is automatically classified into P0 / P1 / P2 priorities. Specifically, when the score of any gated dimension is 0, that gated dimension is unconditionally classified as the highest priority P0.

[0037] As one implementation method, step S5 is followed by step S6: using a Logistic regression prediction model to predict the probability of the website being cited by AI, and outputting a quantitative prediction of "current AI citation probability = 23%, expected to increase to 68% after implementing P0+P1 terms", to provide data support for investment decisions.

[0038] As one implementation, step S5 is followed by step S7: performing EWMA time-series tracking to provide monthly GEO score trends, linear regression prediction of target achievement time, and 2σ anomaly warnings (such as a sudden drop in score caused by changes in competitor algorithms or negative PR events).

[0039] The formula for calculating the overall score is as follows: ... Formula (1); When Pred(i) = Time D i =1; The meanings of each letter in formula (1) are shown in Table 1.

[0040] The above formula defines the Graph-Modulated Aggregation Operator (GMAO) proposed in this invention. It is compared with the classical weighted sum Σw. i ×f i Unlike others, GMAO uses a decay factor D. i (G) Introduces graph topology into aggregation operations, so that the effective contribution of each dimension depends not only on its own score and weight, but also on its position in the causal dependency graph. When the DAG is edgeless, GMAO degenerates into a classic weighted sum.

[0041]

[0042] The scope of the symbols i, j, and k defined in Table 1 is the global default meaning. In formula (13a) (entropy calculation), indices i and k are locally redefined as brand indices (value range 1..m), and j is the dimension index at this time; in formula (14) (DAG decay), i is the dimension index whose decay is calculated, and j is the predecessor dimension index of Pred(i). The specific meanings are subject to the explanation of each formula, and there is no ambiguity.

[0043] Index letter interchange convention: When i, j, and k are all dimension indices (i.e., ∈{1,...,N}), they are interchangeable dummy variables. For example, D i D j D k Both refer to the same variable, "the decay factor of the dimension," differing only in their naming during iteration; w i w j w k Similarly, this refers to "adaptive weights of dimensions"; f i f j f k Similarly, this refers to "non-linear scoring of dimensions." The specific letter used depends on the context of the summation or partial derivative in the formula.

[0044] The evaluation dimensions of this invention include one or more of the following: Blog content assets, EEAT authority signals, external authority signals (a comprehensive measure of backlinks, referring domains, and PR media coverage), Schema structured data, brand reputation signals (integrating brand reputation and sentiment signals from third-party evaluation platforms and community discussion platforms), llms.txt (a standard for LLM-oriented website content index files) deployment status, knowledge graph recognition, video platform evaluation coverage, user-generated content ecosystem, product page GEO readiness, and collection page GEO readiness. Dimension identifiers (non-variables): In this specification, "D1, D2, D3,..., D9" (uppercase D followed by numbers, without subscripts) are the naming identifiers for the dimensions (corresponding to nine dimensions: "Blog Content," "EEAT Authority," "Backlinks and PR," "Schema," "Brand Reputation," "llms.txt," "Knowledge Graph," "Video Evaluation," and "UGC Ecosystem," respectively). The e-commerce extended version adds "D10 Product Page" and "D11 Collection Page." These identifiers are similar to the variable symbol D. i (The subscript indicates the decay factor of dimension i) are different in form and will not be confused.

[0045] The GMAO operator of this invention contains four core mathematical properties and a defect propagation theorem, which are not found in the weighted sum, OWA, Choquet integral and other aggregation operators in the existing MCDM literature.

[0046] Property 1 (Monotonicity): ∂GMAO / ∂f k ≥ 0 holds for all k. Proof: Partial derivatives include the direct contribution w. k ×D k ≥0 and cascade contribution Σ[w i ×f i ×τ {ik} The two parts, / M×...]≥0, are both non-negative. Significance: Improving any dimension will only increase the total score; there is no paradox that "improving A will actually drag down the total score."

[0047] Property 2 (Topological Dependency Score Ceiling): GMAO(f,w,G) ∈ [0, M×C(G)], where C(G)=Σw i ×D i ≤ 1. When there is at least one edge in the DAG and the predecessor score is not full, C(G) < 1, meaning that the theoretical upper limit of the comprehensive score is permanently suppressed by the graph topology. This is a property unique to GMAO, and the classical weighted sum / OWA operators do not have this feature.

[0048] Property 3 (Cascaded Repair Synergistic Effect): Suppose dimension i has two precursors j and k, and the improvement Δ from repairing both simultaneously is... {jk} Strictly greater than the sum of the improvements Δ from individual repairsj +Δ k Reason: The multiplicative decay structure generates a cross term τ. ij ·τ ik (1-f j / M)(1-f k / M), this is the additional benefit when fixing both simultaneously. Business implications: Improving both the blog and backlinks simultaneously has a greater effect on EEAT than the sum of the effects of improving them separately, providing a mathematical basis for optimizing budget allocation.

[0049] Property 4 (Cascade Sensitivity Coefficient CSC): Definition (Direct sensitivity and cascade sensitivity, respectively). Ratio R k = CasSens_k / DirSens_k measures how much of the true importance of dimension k comes from its graph location rather than its weights. In Example 1, D1(Blog) has DirSens_1 = 0.18, CasSens_1 ≈ 0.23, and R1 = 1.28, indicating that the true importance of Blog is 128% higher than its weights. No existing evaluation metric can capture this "implicit importance amplification" caused by graph topology.

[0050] Defect Propagation Theorem: In a DAG, if the score of the root node r is 0, then the decay factor D of each successor node i at depth d is... i ≤ (1-τ min )^d, where τ min This represents the minimum value of the coefficient τ of all edges in the DAG. Specifically, when all edges τ are equal, the DAG... i = (1-τ)^d, meaning the defect propagates exponentially along the graph. In the GEO scenario, if Blog(D1)=0 and assuming the actual scores of all intermediate nodes are also propagated and decayed to 0 in the extreme case: the decay factor of D2(EEAT) at depth 1 ≤ 1-0.70 = 0.30; the decay contribution of D7(KG) at depth 2 along the path D1→D2→D7 ≤ 0.30×(1-0.60) = 0.30×0.40 = 0.12 (Note: the actual decay factor of D7 is the product of all 4 in-degree edges; only the worst-case upper bound of a single path is shown here). A complete defect at a single root node can reduce the effective score of successors at depth 2 by more than 88%.

[0051] The meanings of each letter in the four mathematical properties of the GMAO operator of the present invention are shown in Table 2.

[0052]

[0053] When N=9, the components are arranged in descending order of importance, divided into three tiers: Foundation Layer (D1 Blog Content, D2 EEAT Authority, D3 Backlinks and PR, with a combined weight of approximately 46%), Support Layer (D4 Schema, D5 Brand Reputation, D6 llms.txt, with a combined weight of approximately 30%), and Amplification Layer (D7 Knowledge Graph, D8 Video Reviews, D9 UGC, with a combined weight of approximately 24%). This sorting ensures that almost all directed edges in the DAG point from lower to higher numbers, naturally consistent with topological order. The comparison diagram of the nine-dimensional composite scoring function curves in this embodiment is shown below. Figure 2 As shown. The composite scoring function of the present invention has one or a combination of the following features: (a) the main function is a linear weighted form f = Σαk·Sk, but at least one sub-scoring Sk adopts a nonlinear saturated mapping (including but not limited to log-saturated log(1+x) / log(1+M), exponential time decay exp(-λ·Δt), sigmoid function); (b) the main function is a sigmoid translational normalized form f = 100·max(0, (σ(z)-σ(-β)) / (1-σ(-β))); (c) the main function is a product of binary gating and linear weighting f = δ·Σβk·Qk, where δ∈{0,1}; the common feature of the composite scoring function is that when mapping the original observation index to the continuous scoring domain [0,100], nonlinear transformation is used to ensure diminishing marginal effects, robust outliers, or deployment of gating attributes. Among them, D1 Blog, D2 EEAT, D3 External Links / PR, D5 Reputation, D7 KG, D8 Video, and D9 UGC adopt non-linear sub-scoring + linear main function: f = Σαk·Sk (Sk is saturated with log / exp); D4 Schema (the key defense against shallow deployment score manipulation) adopts sigmoid main function; D6 llms.txt adopts gated × linear main function.

[0054] The dimension set of this invention is scalable. When new influencing factors are generated by the evolution of the AI ​​search ecosystem, the new dimension can be expanded through the following process: (1) Define the original index vector of the new dimension; (2) Select a family of nonlinear functions and calibrate the parameters; (3) Update the AHP (Analytic Hierarchy Process) pairwise comparison matrix to (N+1)×(N+1); (4) Add new nodes and edges to the DAG graph.

[0055] (1) D1 Blog content assets (log saturation function, AHP initial weight 0.18).

[0056] Blog content is the core source of reference for the AI ​​engine, and it has the highest out-degree in the DAG (3 edges → D2, D3, D9), making it the "core" of the entire scoring system.

[0057] ... Formula (2);

[0058] The meanings of each letter in formula (2) are shown in Table 3.

[0059]

[0060] (2) D2 EEAT authoritative signal (multi-layer evidence chain model, AHP initial weight 0.16).

[0061] EEAT stands for "Trust Multiplier," which receives three precursor signals, D1, D3, and D5, and transmits them downstream to the D7 knowledge graph.

[0062] ... Formula (3);

[0063] The meanings of each letter in formula (3) are shown in Table 4.

[0064]

[0065] (3) D3 external authoritative signal - backlinks and PR (mixed logarithmic + exponential saturation, AHP initial weight 0.13).

[0066] PR media coverage is the preferred third-party citation source for the AI ​​engine. There are 2 out-degree edges (→D2 EEAT, →D7 KG).

[0067] ... Formula (4);

[0068] The meanings of each letter in formula (4) are shown in Table 5.

[0069]

[0070] (4) D4 Schema structured data (translated normalized Sigmoid, AHP initial weight 0.12).

[0071] Technical infrastructure, no predecessor dimension. 1 out-degree edge → D7 KG.

[0072] ... Formula (5);

[0073] The meanings of each letter in formula (5) are shown in Table 6.

[0074]

[0075] (5) D5 Brand Reputation Signal (Multi-platform reputation aggregation, AHP initial weight 0.09).

[0076] Twin-model architecture: Sub-model A handles structured reviews such as Trustpilot / Google Reviews, and sub-model B handles community discussions such as Reddit / Quora. One out-degree edge → D2 EEAT.

[0077] ... Formula (6);

[0078] The meanings of each letter in formula (6) are shown in Table 7.

[0079]

[0080] (6) Deployment status of D6 llms.txt (gating dimension, AHP initial weight 0.09).

[0081] This dimension introduces the new concept of "gated dimension". Definition: A gated dimension is a dimension whose scoring function includes a binary deployment indicator δ∈{0,1}. When δ=0, the score of this dimension is always 0, and it triggers unconditional P0 classification, regardless of the calculation result of its priority formula (15). This "hard precondition" mechanism does not exist in traditional MCDM frameworks (AHP, TOPSIS, ELECTRE, etc.) - in traditional MCDM, all dimensions contribute continuously, and there is no model that "forces coverage priority determination when a certain dimension is absent".

[0082] ... Formula (7); The business implications of the gating dimension: The deployment of llms.txt is the "entry ticket," and the superior performance of other dimensions cannot make up for its deficiency.

[0083] The meanings of each letter in formula (7) are shown in Table 8.

[0084]

[0085] (7) D7 Knowledge Graph Recognition (Entity Completeness, AHP Initial Weight 0.09).

[0086] A pure leaf node with 4 in-degree edges (D2, D3, D4, D6) has its score largely determined by the upstream decay factor.

[0087] ... Formula (8);

[0088] The meanings of each letter in formula (8) are shown in Table 9.

[0089]

[0090] (8) D8 video platform evaluation coverage (index saturation + authority, AHP initial weight 0.08).

[0091] ... Formula (9);

[0092] The meanings of each letter in formula (9) are shown in Table 10.

[0093]

[0094] (9) D9 UGC ecosystem (multi-platform convergence, AHP initial weight 0.08).

[0095] A leaf node with two in-degree edges (D1, D8).

[0096] ... Formula (10);

[0097] The meanings of each letter in formula (10) are shown in Table 11.

[0098]

[0099] When the diagnostic target is a DTC e-commerce independent website, product pages and collection pages are the main objects parsed and referenced by the AI ​​shopping assistant (ChatGPTShopping, Perplexity Shopping, Google AI Overviews), accounting for approximately 40% of AI product references. The basic 9-dimensional model's D1 (Blog) only covers informational content, leaving a diagnostic blind spot for pages representing transaction intent. Therefore, for e-commerce scenarios, the model is expanded to N=11, adding the following two dimensions: (10) D10 product page GEO readiness (multi-factor weighted, AHP initial weight 0.12, adaptively adjusted by dual-drive fusion).

[0100] The product page is the core page directly analyzed by the AI ​​shopping engine, and its GEO readiness depends on four sub-metrics: ... Formula (11); The meanings of each letter in formula (11) are shown in Table 12.

[0101]

[0102] (11) GEO readiness of the collection page (log saturation, AHP initial weight 0.08, adaptively adjusted by dual-drive fusion).

[0103] Collection pages (category pages / topic pages) are the preferred source of reference for AI engines to answer queries such as "best XX recommendation".

[0104] ... Formula (12).

[0105] The meanings of each letter in formula (12) are shown in Table 13.

[0106]

[0107] After expanding to N=11, the DAG grows from 9 nodes / 10 edges to 11 nodes / 14 edges, with 4 new causal edges added as shown in Table 14:

[0108] Note: After expanding to N=11, the weights of D10 and D11 are also adaptively adjusted through a dual-driven subjective and objective fusion mechanism: a new 11×11 AHP ​​matrix is ​​constructed to obtain subjective weights, the entropy weight method objective weights are recalculated, and linear fusion is performed using the industry fusion coefficient α. The values ​​of 0.12 and 0.08 mentioned above are only initial values ​​for AHP expert scoring; they will dynamically change according to the data distribution of the e-commerce competition set during actual operation. The original 9-dimensional model remains unchanged as the basic implementation, while the 11-dimensional model serves as the preferred implementation for e-commerce. This extension verifies the aforementioned dimensional scalability design.

[0109] The embodiments of the present invention employ a dual-driven weight fusion method based on subjective and objective factors, which overcomes the shortcomings of the existing technology where the weights remain fixed.

[0110] (1) Subjective weighting: Analytic Hierarchy Process (AHP) Construct a 9×9 pairwise comparison matrix A, with element a ij The importance of dimension i relative to dimension j is represented (Saaty 1-9 scale). The largest eigenvector is calculated as the subjective weight. In a preferred embodiment: The first row of the AHP matrix (D1 Blog vs D1-D9): [1, 1, 1, 2, 2, 2, 2, 2, 3]; The second row of the AHP matrix (D2 EEAT): [1, 1, 1, 1, 2, 2, 2, 2, 2]; The third row of the AHP matrix (D3 outer link): [1, 1, 1, 1, 1, 1, 1, 2, 2]; The fourth row of the AHP matrix (D4 Schema): [1 / 2, 1, 1, 1, 1, 1, 1, 2, 2]; The fifth row of the AHP matrix (D5 reputation): [1 / 2, 1 / 2, 1, 1, 1, 1, 1, 1, 1]; The sixth row of the AHP matrix (D6 llms.txt): [1 / 2, 1 / 2, 1, 1, 1, 1, 1, 1, 1]; The seventh row of the AHP matrix (D7 KG): [1 / 2, 1 / 2, 1, 1, 1, 1, 1, 1, 1]; The eighth row of the AHP matrix (D8 video): [1 / 2, 1 / 2, 1 / 2, 1 / 2, 1, 1, 1, 1, 1]; The ninth row of the AHP matrix (D9 UGC): [1 / 3, 1 / 2, 1 / 2, 1 / 2, 1, 1, 1, 1, 1]; λ max =9.157, CI=(λ max -9) / 8=0.020, CR=CI / RI(9)=0.020 / 1.45=0.014<0.10 ✓; w AHP = [0.177, 0.156, 0.126, 0.116, 0.091, 0.091, 0.091, 0.078, 0.075] (row sum = 1.001, affected by rounding; precise balance is guaranteed by eigenvector normalization).

[0111] This matrix can be adjusted by domain experts based on industry characteristics and will take effect after a consistency check.

[0112] (2) Objective weighting: Entropy weighting method: Based on the standard scores of the target brand and its competitors, the objective weights of each dimension are calculated using information entropy. A higher entropy value indicates a lower distinguishability and a smaller weight for that dimension; conversely, a lower entropy value indicates a larger weight.

[0113] ... Formula (13a); ... Formula (13b);

[0114] Key feature: Objective weights dynamically change based on the actual data distribution of each brand. If all competitors score similarly in the Schema dimension, the objective weight of Schema automatically decreases; if there are significant differences in scores in the Blog dimension, the objective weight of Blog automatically increases.

[0115] (3) Dual-drive fusion and normalization: ...Formula (13c); The significance of the fusion coefficient α: The larger the α, the more it emphasizes expert experience (suitable for B2B industries with scarce data); the smaller the α, the more it emphasizes data-driven approaches (suitable for DTC industries with abundant data). α is determined from the calibration dataset through cross-validation, replacing the original fixed industry adjustment factor lookup table.

[0116] The meanings of the letters in formulas (13c), (13a), and (13b) are shown in Table 15. Index i is locally redefined as the brand index (traversing from 1 to m) within formula (13a), which differs from the global dimension index meaning in formula (1). Index k is in the denominator Σ of formula (13a). k f kj The middle part is the brand index (traversing from 1 to m), and the denominator of formula (13b) is Σ(1-E). k The index j is the dimension index (traversing from 1 to N); index j is always the dimension index.

[0117]

[0118] The causal dependency graph G of this invention contains 9 nodes and 10 directed edges (11 nodes / 14 edges in the e-commerce extended embodiment), and is verified to be acyclic by topological sorting. Attenuation factor formula: ... Formula (14); When Pred(i) = At that time, D i :=1 (empty product convention); Because f j ∈[0,100], each factor (1-τ(1-f / 100))∈[1-τ,1]⊆[0,1], guaranteeing D i ∈[0,1]; The multiplicative compound assumption assumes that the precursor dimensional effects are conditionally independent. In reality, there may be interaction effects between precursors, which can be improved by adding a second-order correction term. The meanings of the letters in formula (14) are shown in Table 16.

[0119]

[0120] As shown in Table 17, the attenuation factor model of this invention is fundamentally different from the conditional probability propagation of Bayesian networks: (1) Bayesian networks propagate evidence bidirectionally through Bayes' theorem, while the attenuation of this invention flows only unidirectionally from predecessor to successor; (2) Bayesian networks require a complete conditional probability table, while this invention only requires one τ coefficient for each edge; (3) Bayesian networks require discretization of the state space, while this invention operates directly on the continuous fractional domain [0,100], with a computational complexity of only O(E) (E is the number of edges).

[0121] The DAG decay model of this invention differs fundamentally from existing dimensional interaction modeling methods (DEMATEL, ISM): DEMATEL (Decision Lab Method) constructs an influence matrix through expert scoring, but its output only adjusts dimensional weights, not the dimensional scores themselves—a weak blog will have its weight increased in DEMATEL, but its EEAT score will not decrease, and the cascading decay effect is completely absent. ISM (Interpretive Structural Model) only outputs a hierarchical diagram and has no quantitative decay calculation capability. The core difference of this invention is that the DAG directly modifies the effective scores of dimensions (rather than weights or hierarchical structures), realizing a novel multi-criteria decision-making mechanism of "cascading penalty for scores."

[0122]

[0123] In Table 17, all directed edges point from lower numbers to higher numbers (except for D5→D2 and D3→D2), which is basically consistent with the topological sort. D2 (EEAT) serves as the "trust convergence point," receiving the three predecessors D1, D3, and D5, reflecting the business logic of "content + backlinks + reputation jointly building authority."

[0124] The parameter calibration method of this invention is as follows: (1) Calibration dataset: Parameter calibration is based on a calibration dataset containing 24 brands, covering three industries: DTC e-commerce (10 brands), B2B industry (7 brands), and SaaS platform (7 brands). Raw indicators of 9 dimensions were collected for each brand, and the actual AI reference results were recorded as supervision labels by performing 50 sampling prompts on each brand on three platforms: ChatGPT, Gemini, and Perplexity.

[0125] (2) Scoring function parameter calibration: Grid Search + 5-fold cross-validation is used. A candidate value grid is defined for the parameters of each scoring function. All parameter combinations are traversed, and the MSE between the model output and the actual AI reference result under each parameter group is calculated. The parameter combination with the smallest average 5-fold MSE is selected.

[0126] (3) DAG structure calibration: DAG edges were constructed using time-lag mutual information analysis to screen candidate edges. Insignificant edges were eliminated using the Bootstrap significance test (p<0.05), and topological sorting was used to verify acyclicity. The τ value was calibrated using standardized regression coefficients. The relative ranking of the decay coefficient λ values ​​(λ... pr =0.008 > λ llms =0.005 > λ blog =0.003) reflects the business pattern that PR's timeliness decays the fastest and Blog's decays the slowest, which has been verified by experiments.

[0127] (4) Sensitivity analysis: Sensitivity analysis was performed on the core parameters with a ±20% perturbation. The results showed that the fluctuation of the final comprehensive score was within ±4.2 points. Among them, the DAG attenuation coefficient τ had the highest sensitivity (±4.2 points), and the logarithmic saturation N... sat The sensitivity is the lowest (±1.3 points). The EWMA (Exponentially Weighted Moving Average) smoothing parameter α=0.3 is determined by minimizing the single-step prediction MSE of historical data, corresponding to a half-life of about 2 months, which is in line with the business rhythm of monthly updates of GEO scores.

[0128] The ablation experiments and comparative analysis are shown in Table 18:

[0129] Conclusion: Nonlinear scoring improves AUC by 11.1%; AHP adaptive weighting further improves AUC by 5.1%; and DAG decay factor is further improved by 10.0%. All three core technical features are indispensable.

[0130] The priority classification calculation formula is: ... Formula (15); Note: Priority i Unnormalized to [0, 100]. The second term w i / max(ε0,D i ) attenuation factor D i The computation is dominated by very small values, which correctly amplifies the urgency of the severe dimension of cascading decay. The threshold of 70 / 45 is empirically calibrated using a calibration dataset. The meanings of the letters in formula (15) are shown in Table 19:

[0131] Please refer to Figure 5 The website GEO readiness quantification diagnostic system of this invention includes a data acquisition module, a standard score calculation module, an adaptive weight calculation module, a decay factor calculation module, and a quantification diagnostic module.

[0132] Data Acquisition Module: Collects raw indicator data for N (N≥3) evaluation dimensions from the target website and related platforms. In specific implementation, the data acquisition module can adopt a three-stage pipeline architecture: Phase 1 (Website Crawling): The system sends an HTTP GET request to the target URL, parses the DOM tree structure of the returned HTML, extracts article node counts using CSS selectors (for D1 Blog scoring), parses the Schema.org JSON-LD structured data blocks embedded in the HTML (for D4 Schema scoring), checks the completeness of the Product / Offer tags on product pages (for D10 product page scoring), extracts structured information such as author biographies, expert certifications, and authoritative awards (for D2 EEAT scoring), and checks the CollectionPage / ItemList Schema deployment and breadcrumb navigation structure on collection pages (for D11 collection page scoring). Data collection follows robots.txt rules and sets a User-Agent identifier.

[0133] Phase Two (Third-Party API Calls): The system calls external data sources via RESTful APIs: It calls the Trustpilot Business API to obtain the number of reviews and star ratings (for D5 reputation scoring); it calls the Google Knowledge Graph Search API to check if the brand entity dashboard exists (for D7 KG scoring); it calls the Ahrefs / SEMrush / Moz APIs to obtain the number of backlinks and DR value (for D3 backlink scoring); it calls the video platform Data API to obtain the number of brand-related review videos (for D8 video scoring); and it calls the APIs of community platforms such as Reddit / Quora to retrieve the number of brand-related discussion posts and sentiment (for D9 UGC scoring). All API responses are parsed in JSON format and include exception status code handling and retry mechanisms.

[0134] The third stage (llms.txt detection): The system sends an HTTP HEAD request to the root directory of the target website to check if the file / llms.txt exists. If it exists, it retrieves and parses its Markdown / YAML structure to verify the content quality and field completeness. The result of this detection directly determines the binary deployment indicator δ∈{0,1} of the gating dimension D6.

[0135] The collected raw data is stored as structured JSON documents, with one record per brand, containing a timestamp, raw indicator vector, and rating result. A relational database (such as PostgreSQL) stores brand profiles, historical rating time-series data, and AHP matrix configuration. A caching layer (such as Redis) caches third-party API responses, with a TTL set to 24 hours to accommodate API rate limits and avoid duplicate requests. A graph database or adjacency matrix stores the DAG structure and τ-coupling strength.

[0136] Standard score calculation module: Maps the raw indicator data of each dimension to the standard score interval [0,100] to obtain the standard score of each dimension.

[0137] Adaptive weight calculation module: Calculates subjective weight vectors based on the analytic hierarchy process and objective weight vectors based on the entropy weight method for each dimension. The subjective and objective weight vectors are then fused using industry calibration fusion coefficients, and the enterprise stage adjustment factor is added and normalized to obtain adaptive weight vectors for each dimension.

[0138] Decay factor calculation module: Constructs a causal dependency directed acyclic graph G for each dimension, calculates the dependency decay factor for each dimension, where the dependency decay factor is the product of the decay contributions of the predecessor dimension. The diagnostic output of this invention is not only a score and priority classification, but also includes an automatically generated causal explanation chain, revealing the root cause path for each P0 dimension. This explanation chain is generated by recursively backtracking from the P0 dimension towards the predecessor in the DAG: Algorithm flow: For each P0 dimension k, traverse its predecessor set Pred(k), filter out predecessors that contribute significantly to decay (i.e. (1-τ·(1-f_j / 100))<0.7, indicating that the predecessor caused more than 30% decay), add the filtered predecessors to the interpretation chain, and recursively backtrack its predecessors until the source node with no predecessor is reached.

[0139] Example output: "D7(KG)=15 points [P0] ← Because D2(EEAT) effective score only 38 decayed to 12.5 (decay of 67%) ← Because D1(Blog)=45 and D3(backlinks)=40 are both low." This causal explanation chain is a diagnostic output that no existing SEO / GEO tool can provide, because existing tools do not model the causal relationships between dimensions.

[0140] The quantitative diagnostic module calculates a comprehensive score by weighting and summing the standard scores, adaptive weights, and dependency decay factors among the dimensions. It also automatically classifies each dimension into different priorities based on the target gap rate, weight amplification factor, and causal transmission degree. The scoring calculation adopts a task queue architecture (such as Celery / RabbitMQ) to achieve parallelization. The specific process is as follows: (1) After receiving the diagnostic request, the scheduler distributes the scoring tasks of N dimensions to the parallel work queue. Each worker executes the composite scoring function of the corresponding dimension. (2) After all dimensions are scored, they are aggregated into a score vector f[]. (3) The dual-drive weight module calculates the weight vector w[]. (4) The DAG engine performs topological sorting on the graph nodes and calculates the decay factor D_i of each dimension in order. (5) The GMAO aggregation module receives three typed input vectors f[], w[], and D[], performs the Σw_i×f_i×D_i operation, and outputs GEO_Score. (6) The priority classifier and the causal chain generator are executed in parallel. The overall computational complexity is O(N+E), and the diagnosis time for a single brand is controllable.

[0141] The system of this invention provides a RESTful API interface: diagnostic requests are submitted via the POST / api / v1 / diagnose interface, with the request body in JSON format, containing fields such as url (target website), industry (industry category), and competitors[] (competitor list). The response body returns structured JSON, containing geo_score (floating-point composite score), dimensions[] (raw scores, effective scores, and decay factors for each dimension), priorities[] (P0 / P1 / P2 classification results), and causal_chains[] (causal explanation chain sequence). Batch diagnostics are submitted via the POST / api / v1 / batch interface, executed asynchronously, and completed through Webhook callback notifications.

[0142] The system also provides a PDF / HTML diagnostic report generation module (including radar charts, DAG visualization, and optimization roadmaps) and a WebSocket real-time push interface (for pushing progress updates to the dashboard during monthly rescans). The output interface supports plug-in integration with mainstream SEO platforms (such as Shopify and WordPress).

[0143] The system of the present invention can be deployed in the following ways: (1) Cloud-native SaaS deployment: microservice architecture, each module (crawler, scoring engine, GMAO calculation, report generation) is independently containerized and decoupled through message queue, supporting horizontal scaling; (2) Platform embedded deployment: embedded as SDK or microservice into SEO SaaS platform (such as Ahrefs, SEMrush and similar products) or e-commerce platform (such as Shopify App), and accessed through API gateway; (3) Private deployment: for data-sensitive enterprise customers, Docker containerized private deployment is supported, and all data remains in the customer environment.

[0144] As one implementation method, the standard score calculation module uses a composite scoring function specific to each dimension for mapping, and the composite scoring function has one of the following characteristics: (a) The main function is in linear weighted form ,in This is the composite score output for this assessment dimension. For the first The weighting coefficients corresponding to individual scores For the first Individual sub-rating; at least one sub-rating A nonlinear saturation mapping is employed, which includes logarithmic saturation, exponential time decay, or sigmoid functions. (b) The main function is in the sigmoid translation-normalized form. ,in This is the composite score output for this assessment dimension. For the sigmoid function, This is the input to the sigmoid function. This is the offset of the sigmoid function; (c) The main function is a product of a binary gate and a linearly weighted function. ,in This is the composite score output for this assessment dimension. To deploy indicators, For the first The weighting coefficients corresponding to individual scores For the first A non-negative score.

[0145] For example, the composite scoring function specific to the blog content asset assessment dimension is: ; Among them, f blog S is used to score and output the content asset evaluation dimension of blogs. qty and These are sub-scores for the number of blog posts and their weights. and These are the sub-scores for blog content quality and their weights. and These are the Answer-Ready scores and their weights. and These are timeliness sub-scores and their weights; The EEAT authoritative signal assessment dimension's exclusive composite scoring function is as follows: ; in, EEAT authoritative signal assessment dimension score output, For the score of the k-th type of evidence, Let k be the weight of the k-th type of evidence, where k is the index of the evidence type, and k ∈ {1,2,3,4}; The composite scoring function specific to the external authoritative signal evaluation dimension is as follows: ; in, The scoring output is based on the evaluation dimensions of external authoritative signals. and These are the sub-scores for the number of antilinks and their weights. and These are the number of sub-scores for the recommendation domain and their weights. and These are the sub-scores and weights for PR activities. and These are the PR timeliness sub-scores and their weights; The composite scoring function specific to the evaluation dimensions of schema-structured data is as follows: ; in, The output of the Schema structured data evaluation dimension score is σ(x), which represents the sigmoid function, z is the input of the sigmoid function, and β is the offset of the sigmoid function. The specific composite scoring function for brand reputation signal assessment dimensions is as follows: ; in, Output scores for brand reputation signal evaluation dimensions. and These are the comment sub-ratings and their weights. and These are the community discussion sub-ratings and their weights. and These are sub-scores and their weights for the brand's response quality to reviews / word-of-mouth. The composite scoring function specific to the deployment status assessment dimension in llms.txt is as follows: ; in, Output scores for the deployment status assessment dimensions in llms.txt. and These are the core page coverage quality scores and their weights in llms.txt. and These are the Markdown structured quality scores and their weights for llms.txt. and These are the update timeliness quality scores and their weights for llms.txt; The specific composite scoring function for the knowledge graph recognition evaluation dimension is as follows: ; in, The output score is for the knowledge graph recognition evaluation dimension. and These are the Google Knowledge Panel entity recognition scores and their weights. and These are the Wikidata / Wikipedia attribute completeness scores and their weights. and These are the entity association connection density scores and their weights; The video platform evaluation covers specific composite scoring functions for various assessment dimensions. ; in, To provide video platform evaluations and assessments, including scoring outputs across various dimensions. and These are the sub-ratings covered by the number of video platform evaluations and their weights. and These are the sub-scores and their weights for sentiment NLP analysis of video comment sections. and These are sub-scores for evaluating the creator's authority and their weights; The composite scoring function specific to the user-generated content ecosystem assessment dimension is as follows: ; in, Generate content ecosystem assessment scores for users. and These are the sub-ratings of UGC density within the site and their weights. and These are the sub-scores of external UGC quality and their weights. and These are the quality scores and weights of the brand's UGC responses; The specific composite scoring function for the GEO readiness assessment dimension of the product page is as follows: ; in, S scores are output for the GEO readiness assessment dimensions of the product page. pschema and These are the Product / Offer Schema completeness and validity scores and their weights, S desc and These are AI-parseable descriptions of depth scores and their weights; S compare and These are the comparison of content coverage scores and their weights; S qa and These are the product FAQ coverage scores and their weights; The composite scoring function specific to the GEO readiness assessment dimension of the collection page is as follows: ; in, For the GEO readiness assessment dimension score output of the collection page, S catschema and These are the deployment rate scores and weights for CollectionPage / ItemList Schemas; S guide and These are the content quality scores and weights for category-level buying guides; S interlink and These are cross-category internal chain density scores and their weights; S breadcrumb and These are the breadcrumb navigation schema and the hierarchy depth score and its weight, respectively.

[0146] As one implementation method, the adaptive weight calculation module constructs a multi-dimensional pairwise comparison matrix based on the analytic hierarchy process (AHP) and calculates the largest eigenvector as the subjective weight vector; it also calculates the objective weight vector of the target brand in the competition set based on the entropy weight method. ; ; Among them, E j Let E be the information entropy of dimension j on the set of competing brands. k Let m be the information entropy of dimension k, m be the number of brands in the competition set, i be the brand index, and p be the information entropy of dimension k. ij w represents the normalized score ratio of brand i on dimension j. entropy j Let the objective entropy weight be dimension j; Linear fusion is performed according to the following formula: ; Among them, w j For the final adaptive weight of dimension j, Let the subjective AHP weights of dimension j be , α is the adjustment factor for the enterprise life cycle stage in dimension j, and α is the industry calibration fusion coefficient.

[0147] As one implementation method, the dependent attenuation factor is calculated according to the following formula: ; in, Let i be the decay factor of dimension i. Let be the attenuation intensity coefficient on the directed edge from dimension j to dimension i in the DAG. Let Pred(i) be the nonlinear score for dimension j, Pred(i) be the set of predecessor nodes for dimension i, and Π be the chain multiplication operator. The quantitative diagnostic module calculates the comprehensive score according to the following formula: ; Among them, GEO Score The website's overall GEO readiness score, w i For dimension i, the subjective and objective dual-driven adaptive weights are used. The output of the composite scoring function for dimension i. Let i be the causal dependency decay factor of dimension i in graph G; Priority classification is based on the following formula: ; in, Let i be the priority score for dimension i. , , ε is the weight coefficient; |Succ(i)| is the number of successor nodes in dimension i; Target is the target full score threshold; ε0 is the zero-avoidance constant (to prevent division by zero when D_i is extremely small). Each dimension is categorized into P0 / P1 / P2 priorities: P0: Priority ≥ 70 or gated dimension score = 0; P1: 45 ≤ Priority < 70; P2: Priority < 45.

[0148] Example 1, DTC e-commerce brand A: Brand A is a DTC (Direct-to-Contact) model smart pet supplies e-commerce platform. Nine-dimensional standard score: D1 (Content) = 45, D2 (EEAT) = 38, D3 (External Links) = 40, D4 (Schema) = 62, D5 (Reputation) = 48, D6 (llms.txt) = 75, D7 (KG) = 15, D8 (Video) = 42, D9 (UGC) = 33.

[0149] Example of calculation for step S4: Taking D2(EEAT) as an example, its predecessors are D1 (τ=0.70), D3 (τ=0.50), and D5 (τ=0.45).

[0150] D2 = (1-0.70×(1-45 / 100)) × (1-0.50×(1-40 / 100)) × (1-0.45×(1-48 / 100)) = (1-0.70×0.55) × (1-0.50×0.60) × (1-0.45×0.52) = 0.615 × 0.700 × 0.766 = 0.330; The effective EEAT score is 38 × 0.330 = 12.5, which is significantly lower than the original score of 38, reflecting the cascading drag on EEAT caused by weak blog / backlinks.

[0151] After applying a dual-drive weighted fusion using a DTC industry fusion coefficient α=0.55, the GMAO operator calculates a GEO_Score of 42.1 / 100 and a GMAO topology ceiling C(G) of 0.87, indicating that cascading dependence reduces brand A's theoretical maximum score to 87. Priority: D7 (KG) and D2 (EEAT) are P0, D1 (Blog) and D9 (UGC) are P1. Core insight: Blog content (D1=45), as the highest weighted dimension, scores low, dragging down EEAT and UGC through the DAG, resulting in cascading decay.

[0152] Example 2, B2B Industrial Brand B: Brand B is a B2B industrial automation component supplier. Nine-dimensional standard score: D1 (Content) = 12, D2 (EEAT) = 22, D3 (External Links) = 18, D4 (Schema) = 28, D5 (Reputation) = 12, D6 (llms.txt) = 0, D7 (KG) = 10, D8 (Video) = 8, D9 (UGC) = 10. A B2B industry integration coefficient α = 0.70 (emphasizing expert experience) is used, and GEO_Score = 13.5 / 100. D6 (llms.txt) is unconditionally judged as P0 due to a gating dimension score of 0.

[0153] Example 3, SaaS Brand C: Brand C is a 3D virtual house viewing SaaS platform. The nine-dimensional standard score is: D1 (Content) = 35, D2 (EEAT) = 40, D3 (External Links) = 35, D4 (Schema) = 55, D5 (Reputation) = 28, D6 (llms.txt) = 0, D7 (KG) = 30, D8 (Video) = 25, D9 (UGC) = 22. A SaaS industry integration coefficient α = 0.60 (balanced mode) and GEO_Score = 27.8 / 100 are used. This demonstrates the differentiated diagnostic results of the same model across different industries.

[0154] The nine-dimensional radar scoring comparison diagram of the three embodiments of the present invention is shown below. Figure 4 As shown.

[0155] Comparative Example 1, Dimensional Dependency Analysis Based on PCA: Principal Component Analysis (PCA) is the most common method for discovering relationships between dimensions. However, PCA can only capture linear correlations and cannot identify causal direction. For example, PCA might find that Blog and EEAT are "correlated," but it cannot determine whether it's Blog→EEAT or EEAT→Blog. The DAG attenuation method of this invention requires precisely a directional causal relationship—"Blog is weak and drags down EEAT"—rather than the reverse. On the same dataset, the AUC of the PCA scheme is only 0.695, significantly lower than the 0.823 of this invention.

[0156] For Example 2, weight adjustments based on correlation coefficient: Using the Pearson correlation coefficient matrix to adjust dimensional weights is another "obvious" attempt. However, this method suffers from a serious multicollinearity problem: the high correlation between Blog and EEAT leads to unstable weight estimations, and even small sample perturbations can produce drastically different weight assignments. More importantly, the correlation coefficient can only tell us that the two dimensions "rise and fall together," but it cannot reveal the core "cascading decay effect" of this invention—that is, how a low score in one dimension mathematically drags down the effective score in another dimension.

[0157] Comparative Example 3: End-to-end learning of neural networks: Using a deep learning model to directly predict AI citation probability from raw metrics is the third "obvious" attempt. However, this approach has three fatal problems: (1) the sample size of 24 brands is far from sufficient to support neural network training, and serious overfitting occurs in actual tests; (2) the neural network is a "black box" and cannot output the interpretable P0 / P1 / P2 priority classification of the core of this invention; (3) it cannot provide the causal cascade prediction ability of "improving which dimension will drive which downstream dimensions".

[0158] Comparative Example 4, DEMATEL Decision Laboratory Method: DEMATEL is a classic method for handling inter-dimensional interactions in multi-criteria decision-making and also constructs a directed influence graph. However, DEMATEL's outputs are "influence" and "affected," used to adjust dimension weights—a weak blog will increase its weight in DEMATEL but will not decrease the EEAT score. DEMATEL cannot answer the core question: "How much will the effective EEAT score be dragged down when blog = 45?" This invention's DAG directly modifies the effective score of a dimension (rather than its weight), implementing a mechanism of "score cascading penalty" that is completely absent in DEMATEL.

[0159] Comparative Example 5, ISM Explanation of Structural Model: ISM can also construct hierarchical structure diagrams between dimensions, but its output is purely qualitative structural relationships ("Blog affects EEAT"), without any quantitative attenuation calculations. ISM cannot answer "how much influence there is" or "what is the superposition effect when multiple precursors are weak at the same time". The multiplicative attenuation model of this invention not only provides directional causal relationships, but also accurately quantifies the attenuation intensity (τ coefficient) of each edge, and achieves superposition attenuation of multiple precursors through multiplicative composition.

[0160] The comparison of the five schemes above demonstrates that all known methods that a person skilled in the art would "obviously try"—whether general data analysis methods (PCA, correlation coefficient, neural networks) or specialized multi-criteria decision-making methods (DEMATEL, ISM)—cannot solve the core technical problem addressed by this invention, namely, "quantitative modeling of cascading causal decay across dimensions, discovery of hidden bottlenecks, and generation of causal attribution chains." The GMAO operator and DAG multiplicative decay model of this invention are not a simple combination of known methods, but rather an original modeling of causal relationships in the GEO domain. None of the five alternative schemes can equivalently achieve the "score cascading decay" mechanism of this invention—that is, the same diagnostic results cannot be obtained through linear weighting, weight adjustment, or any existing aggregation operator. This invention produces two unexpected technical effects: a qualitative mutation from P2 to P0 and a 67% cascading decay.

[0161] This invention has broad industrial application value. The following four typical scenarios illustrate the practical problems it solves.

[0162] Scenario 1: GEO Health Diagnosis and Optimization Guidance for E-commerce Independent Websites: Application Background: DTC brands and independent cross-border e-commerce websites invest heavily in Google Ads and traditional SEO, but are completely absent from product recommendations in AI search engines like ChatGPT and Perplexity. Brands are unaware of the reasons or where to begin optimization. Existing SEO tools (Ahrefs, SEMrush, etc.) cannot diagnose this problem because their scoring systems do not incorporate the evaluation dimensions unique to AI engines.

[0163] Solution: This invention's method performs a 9-dimensional GEO diagnosis on the independent website. For example, the diagnosis results show that D1 (Blog) = 25 is the root cause, which, through the DAG causal chain, drags down D2 (EEAT) = 18 and D9 (UGC) = 12. The P0 / P1 / P2 priority classification provides a clear optimization roadmap: "First deploy llms.txt (2 hours of work), then publish 50 Answer-Ready articles (2 weeks of work)." This is an optimization guidance based on causal dependency analysis that no existing tool can provide.

[0164] Specifically, for cross-border e-commerce, this method can quantify and diagnose the precise gaps in "market GEO signals" (such as a brand reputation score of 5 indicating zero Trustpilot reviews and zero Reddit presence), and the DAG causal chain provides a sequential roadmap for development.

[0165] For independent e-commerce websites, an N=11 extended implementation can be adopted. The newly added D10 (product page) and D11 (collection page) can diagnose the transaction intent pages most frequently referenced by the AI ​​shopping assistant, covering the diagnostic blind spots of high-commercial-value queries such as "best [product] under $300" and "compare [brand] vs [competitor]". This increases the diagnostic coverage of the method of this invention in e-commerce scenarios from approximately 60% to over 95%.

[0166] Scenario 2, Multi-brand Competition Benchmark Analysis: Application Background: Before entering a new market, brand strategy teams need to understand the distribution of competitors' GEO capabilities and find dimensions where they can differentiate themselves.

[0167] Solution: Run GEO diagnostics on five or more competitors simultaneously. The entropy weight method's objective weighting component automatically identifies which dimensions have the greatest discriminative power within this specific competitive set. For example, if all competitors have high blog scores but generally low video coverage, the entropy weight of the video dimension automatically increases, signaling that "video platform reviews are a better opportunity." This data-driven competitive insight is something that fixed-weight systems cannot provide.

[0168] Scenario 3: Tracking GEO optimization effects and measuring return on investment: Application Background: Enterprises invest significant resources in GEO optimization (e.g., $10,000 per month for content creation and PR campaigns), and management needs quantifiable progress reports and time-to-achieve forecasts.

[0169] Solution: EWMA time-series tracking (S7) provides monthly GEO score trends, linear regression predictions of target achievement time, and 2σ anomaly warnings (such as sudden score drops caused by competitor algorithm changes or negative PR events). The Logistic regression prediction model (S6) outputs a quantitative prediction that "the current AI citation probability is 23%, and it is expected to increase to 68% after implementing the P0+P1 term," providing data support for investment decisions.

[0170] Scenario 4: Platform-level batch diagnostics and service productization: Application Background: SEO SaaS platforms (such as Ahrefs and SEMrush) need to add an "AI Visibility" feature module; e-commerce platforms (such as Shopify) want to help sellers improve AI discoverability; digital marketing agencies need to transform GEO diagnostics from expert consultation into a scalable service product.

[0171] Solution: The method of this invention can be embedded as a core computing module into the aforementioned platform. Its dual-driven subjective and objective weighting mechanism can automatically adapt to the different industry and competitive environments of different sellers, without the need for manual parameter tuning. After the platform runs batch diagnostics, each seller receives personalized P0 / P1 / P2 optimization suggestions. DAG causal visualization ("Your blog's weakness is cascading down EEAT and UGC") is a product differentiation force that cannot be matched by a single numerical score. The dimensional scalable architecture (N≥3) allows the platform to add platform-specific dimensions based on its own ecosystem.

[0172] 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 quantitative diagnostic method for website GEO readiness, characterized in that, Includes the following steps: S1: Collect raw indicator data from multiple evaluation dimensions from the target website and related platforms; S2: Map the raw indicator data of each dimension to the standard score interval [0,100] to obtain the standard score of each dimension; S3: Calculate the subjective weight vector and objective weight vector for each dimension, linearly fuse the subjective weight vector and objective weight vector, superimpose the enterprise stage adjustment factor and normalize it to obtain the adaptive weight vector for each dimension. S4: Construct a causal dependency directed acyclic graph G between each dimension, and calculate the dependency decay factor for each dimension, wherein the dependency decay factor is the product of the decay contributions of the predecessor dimension. S5: The standard scores, adaptive weights, and dependency decay factors of each dimension are weighted and summed to obtain a comprehensive score, and each dimension is automatically classified into different priorities based on the target gap rate, weight amplification factor, and causal transmission degree. The evaluation dimensions include one or more of the following: Blog content assets, EEAT authoritative signals, external authoritative signals, Schema structured data, brand reputation signals, llms.txt deployment status, knowledge graph recognition rate, video platform evaluation coverage, user-generated content ecosystem, product page GEO readiness, and collection page GEO readiness.

2. The website GEO readiness quantification diagnostic method as described in claim 1, characterized in that, In step S2, a composite scoring function specific to each dimension is used for mapping. The composite scoring function has one of the following characteristics: (a) The main function is in linear weighted form ,in This is the composite score output for this assessment dimension. For the first The weighting coefficients corresponding to individual scores For the first Individual sub-rating; at least one sub-rating A nonlinear saturation mapping is employed, which includes logarithmic saturation, exponential time decay, or sigmoid functions. (b) The main function is in the sigmoid translation-normalized form. ,in This is the composite score output for this assessment dimension. For the sigmoid function, This is the input to the sigmoid function. This is the offset of the sigmoid function; (c) The main function is a product of a binary gate and a linearly weighted function. ,in This is the composite score output for this assessment dimension. To deploy indicators, For the first The weighting coefficients corresponding to individual scores For the first A non-negative score.

3. The website GEO readiness quantification diagnostic method as described in claim 1, characterized in that, In step S3, a multi-dimensional pairwise comparison matrix is ​​constructed based on the analytic hierarchy process (AHP), and the largest eigenvector is calculated as the subjective weight vector. The objective weight vector of the target brand in the competition set is calculated using the entropy weight method. ； ； Among them, E j Let E be the information entropy of dimension j on the set of competing brands. k Let m be the information entropy of dimension k, m be the number of brands in the competition set, i be the brand index, and p be the information entropy of dimension k. ij w represents the normalized score ratio of brand i on dimension j. entropy j Let the objective entropy weight be dimension j; Linear fusion is performed according to the following formula: ； Among them, w j For the final adaptive weight of dimension j, Let the subjective AHP weights of dimension j be , α is the adjustment factor for the enterprise life cycle stage in dimension j, and α is the industry calibration fusion coefficient.

4. The website GEO readiness quantification diagnostic method as described in claim 1, characterized in that, The dependent decay factor is calculated according to the following formula: ； in, Let i be the decay factor of dimension i. Let be the attenuation intensity coefficient on the directed edge from dimension j to dimension i in the DAG. Let Pred(i) be the nonlinear score for dimension j, Pred(i) be the set of predecessor nodes for dimension i, and Π be the chain multiplication operator. In step S5, the comprehensive score is calculated according to the following formula: ； Among them, GEO Score The website's overall GEO readiness score, w i For dimension i, the subjective and objective dual-driven adaptive weights are used. The output of the composite scoring function for dimension i. Let i be the causal dependency decay factor of dimension i in graph G; Priority classification is based on the following formula: ； in, Let i be the priority score for dimension i. , , is the weight coefficient; |Succ(i)| is the number of successor nodes in dimension i, Target is the target full score threshold, and ε0 is the zero-avoidance constant; Each dimension is categorized into P0 / P1 / P2 priorities: P0: Priority ≥ 70 or gated dimension score = 0; P1: 45 ≤ Priority < 70; P2: Priority < 45.

5. A quantitative diagnostic system for website GEO readiness, characterized in that, include: Data acquisition module: Collects raw indicator data from multiple evaluation dimensions from the target website and related platforms; Standard score calculation module: Maps the raw indicator data of each dimension to the standard score interval [0,100] to obtain the standard score of each dimension; Adaptive weight calculation module: Calculates the subjective weight vector and objective weight vector for each dimension, linearly merges the subjective weight vector and objective weight vector, superimposes the enterprise stage adjustment factor and normalizes it to obtain the adaptive weight vector for each dimension. Decay factor calculation module: Constructs a causal dependency directed acyclic graph G for each dimension, calculates the dependency decay factor for each dimension, whereby the dependency decay factor is the product of the decay contributions of the predecessor dimension. Quantitative Diagnosis Module: The module calculates a comprehensive score by weighting and summing the standard scores, adaptive weights, and dependency decay factors of each dimension, and automatically classifies each dimension into different priorities based on the target gap rate, weight amplification factor, and causal transmission degree. The evaluation dimensions include one or more of the following: Blog content assets, EEAT authoritative signals, external authoritative signals, Schema structured data, brand reputation signals, llms.txt deployment status, knowledge graph recognition rate, video platform evaluation coverage, user-generated content ecosystem, product page GEO readiness, and collection page GEO readiness.

6. The website GEO readiness quantification diagnostic system as described in claim 5, characterized in that, The standard score calculation module uses a composite scoring function specific to each dimension for mapping, and the composite scoring function has one of the following characteristics: (a) The main function is in linear weighted form ,in This is the composite score output for this assessment dimension. For the first The weighting coefficients corresponding to individual scores For the first Individual sub-rating; at least one sub-rating A nonlinear saturation mapping is employed, which includes logarithmic saturation, exponential time decay, or sigmoid functions. (b) The main function is in the sigmoid translation-normalized form. ,in This is the composite score output for this assessment dimension. For the sigmoid function, This is the input to the sigmoid function. This is the offset of the sigmoid function; (c) The main function is a product of a binary gate and a linearly weighted function. ,in This is the composite score output for this assessment dimension. To deploy indicators, For the first The weighting coefficients corresponding to individual scores For the first A non-negative score.

7. The website GEO readiness quantification diagnostic system as described in claim 5, characterized in that, The adaptive weight calculation module constructs a multi-dimensional pairwise comparison matrix based on the analytic hierarchy process (AHP) and calculates the largest eigenvector as the subjective weight vector; it also calculates the objective weight vector of the target brand in the competition set based on the entropy weight method. ； ； Among them, E j Let E be the information entropy of dimension j on the set of competing brands. k Let m be the information entropy of dimension k, m be the number of brands in the competition set, i be the brand index, and p be the information entropy of dimension k. ij w represents the normalized score ratio of brand i on dimension j. entropy j Let the objective entropy weight be dimension j; Linear fusion is performed according to the following formula: ； Among them, w j For the final adaptive weight of dimension j, Let the subjective AHP weights of dimension j be , α is the adjustment factor for the enterprise life cycle stage in dimension j, and α is the industry calibration fusion coefficient.

8. The website GEO readiness quantification diagnostic system as described in claim 5, characterized in that, The dependent decay factor is calculated according to the following formula: ； in, Let i be the decay factor of dimension i. Let be the attenuation intensity coefficient on the directed edge from dimension j to dimension i in the DAG. Let Pred(i) be the nonlinear score for dimension j, Pred(i) be the set of predecessor nodes for dimension i, and Π be the chain multiplication operator. The quantitative diagnostic module calculates the comprehensive score according to the following formula: ； Among them, GEO Score The website's overall GEO readiness score, w i For dimension i, the subjective and objective dual-driven adaptive weights are used. The output of the composite scoring function for dimension i. Let i be the causal dependency decay factor of dimension i in graph G; Priority classification is based on the following formula: ； in, Let i be the priority score for dimension i. , , is the weight coefficient; |Succ(i)| is the number of successor nodes in dimension i, Target is the target full score threshold, and ε0 is the zero-avoidance constant; Each dimension is categorized into P0 / P1 / P2 priorities: P0: Priority ≥ 70 or gated dimension score = 0; P1: 45 ≤ Priority < 70; P2: Priority < 45.

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