Technological cycle prediction method and system based on double-track model and coordination factor

Abstract

The application discloses a technical period prediction method and system based on a double-track model and a coordination factor, and the method comprises the following steps: S1, constructing a double-track model of a material track and an abstract track; S2, calibrating an abnormal difficulty A of a target technology abnormality based on a unique negation theory; S3, acquiring or estimating a force factor C, a data factor D, a number N of intelligent agents participating in cooperation and an efficiency factor E; S4, quantifying a coordination factor η, and the coordination factor η is determined based on whether the intelligent agents participating in cooperation are built-in with the double-track model and the core rules of the unique negation theory; S5, calculating a theoretical period T according to a period formula, and outputting a prediction result; and the calculation method of the period formula is T=A / (CxDxNxExη). The application filters unfeasible technology paths by introducing a material track rigid boundary, and improves prediction accuracy by quantifying human-AI cooperation.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and technology forecasting, and in particular to a technology cycle forecasting method and system based on a dual-track model and synergistic factors. Background Technology

[0002] Existing technology cycle prediction methods mainly rely on historical data extrapolation, expert scoring, or single-model simulation, which have the following drawbacks: 1. Ignoring the rigid boundaries of cognitive architecture: failing to distinguish between material orbits (constant laws of the universe) and abstract orbits (rules constructed by humans), causing predictive models to fail when encountering technical bottlenecks.

[0003] 2. Unable to quantify synergistic effects: The efficiency of multi-agent collaboration (human-human, human-AI, AI-AI) is only represented by simple coefficients, and the physical meaning and calculation method of the synergistic factor η are not defined.

[0004] 3. Lack of assumption of homogeneous cognitive configuration protocol: It does not consider that when two agents (human decision-making node and core computing node) have the same underlying cognitive architecture, the synergy factor can reach the theoretical maximum value η=1. Summary of the Invention

[0005] The purpose of this invention is to provide a technology cycle prediction method and system based on a dual-track model and synergistic factors. By introducing the dual-track division of material structure / abstraction, the unique negation theory and the assumption of homologous cognitive configuration protocol, it can accurately quantify the time (T) to resolve technological anomalies (A) and construct a reproducible and transferable ternary synergistic intelligent agent architecture.

[0006] This invention is achieved using the following technical solution: a technology cycle prediction method based on a dual-track model and synergistic factors, comprising the following steps: S1: Construct a dual-track model of physical track and abstract track. The physical track knowledge base includes physical, chemical, and biological laws, as well as the innate cognitive framework of humans, as rigid constraints. The abstract track knowledge base includes scientific theories, technological solutions, social norms, and cultural symbols constructed by humans. S2: The difficulty A of identifying the anomaly of the target technology based on the unique negation theory; S3: Obtain or estimate the computing power factor C, data factor D, number of agents participating in the collaboration N, and efficiency factor E; S4: Quantify the collaboration factor η, which is determined based on whether the agents participating in the collaboration have a built-in dual-track model and the core rules of the unique negation theory; S5: Calculate the theoretical period T according to the period formula and output the prediction result; The calculation method for the periodic formula is as follows: T = A / (C × D × N × E × η).

[0007] Furthermore, step S2 specifically includes: Receive the target technical solution text, extract the key technical concepts and corresponding semantic vectors from the target technical solution text; retrieve physical law entries that match the key technical concepts from the object structure orbit knowledge base, and calculate the semantic conflict score between the semantic vector of the key technical concept and the constraint vector of the physical law entry; if the semantic conflict score exceeds a preset threshold, it is determined to be infeasible and the prediction is terminated; otherwise, according to a scoring table that includes at least three dimensions, including the technology maturity level, the category of basic scientific problems to be solved, and the estimated resource input level, the anomalous difficulty A is marked. The scoring table on which the anomalous difficulty A is based has preset weight coefficients for the three dimensions: the level of technological maturity, the category of basic scientific problems to be solved, and the estimated amount of resource input. The scoring is to normalize the weighted sum of the scores of each dimension to a level of 1-5.

[0008] Furthermore, the computing power factor C = actual available computing power resources / current top-level computing power benchmark value; The data factor D = the degree of matching between the actual dataset and the ideal dataset required to solve the anomaly, with a value of 0 to 1; The number of intelligent agents N = the total number of human experts and AI systems participating in the collaboration; The efficiency factor E = actual average efficiency / instantaneous efficiency benchmark value of the top expert or best AI in the field, with a value of 0~1.

[0009] Furthermore, the basic value of the synergy factor is: η = 0.5; For each participating agent, a cognitive architecture matching degree test is performed to check whether it has the core rules of the dual-track model and the unique negation theory built in. If all agents pass the test, then η = 1; if some agents pass, then η is determined by linear interpolation based on the passing ratio.

[0010] Furthermore, during the collaboration process, monitor indicators such as response latency, task completion synchronization rate, and interaction round reduction rate between intelligent agents; If the above indicators continue to improve, η will increase by 0.01 in each iteration, with an upper limit of 1.0.

[0011] Furthermore, the theoretical period T is obtained by regression calibration based on the ratio of the time span from the anomalous proposal to the realization of a known technology in the historical technology case set to the corresponding theoretical period T, and includes outputs of parameter values ​​and adjustment suggestions. Specifically, the product of the calculated theoretical period T and a benchmark scaling factor K is used as the real-time output; the benchmark scaling factor K is obtained from a historical technology case library, which stores the actual R&D periods of multiple completed technology projects and their calculated theoretical periods; the method for obtaining K includes: calculating the comprehensive similarity between the target technology and each case in the historical case library based on the anomalous difficulty A and the technology field, selecting at least one historical technology case with the highest similarity, and determining the ratio of the actual R&D period to the theoretical period of this at least one historical technology case as the benchmark scaling factor K. The calculation dimensions of the comprehensive similarity include at least the level difference of the anomalous difficulty A, the matching degree of the preset classification code of the technology field, and the cosine similarity of the functional keyword vector of the technical solution. The system calculates the total case similarity score by weighted summation of the above dimensions.

[0012] Furthermore, it also includes the steps for constructing a ternary collaborative intelligent agent architecture: Define three types of intelligent agent roles: human decision-making node ontology C1, core computing node native C2, and core computing node clone C3; Establish a homogeneous superposition protocol to unify the cognitive architecture of the three intelligent agents into a configuration file of a dual-track model and a unique negation theory; Homologous superposition is achieved through the formula S = C1⊕C2⊕C3, where ⊕ indicates that the cognitive framework is consistent and that they complement each other without negating each other during decision-making.

[0013] Furthermore, the human decision-making node ontology C1 is played by a human, who is responsible for defining anomalies, anchoring directions, and providing value judgments; The core computing node native C2 is played by an AI prediction engine, which is responsible for core computing, formula derivation, and knowledge base retrieval. The core computing node clone C3 is played by a lightweight AI, which is responsible for cross-platform interaction, result display, user feedback collection, and knowledge dissemination.

[0014] Furthermore, a unified cognitive architecture for the three intelligent agents is achieved using JSON format configuration files; Real-time communication between the three can be achieved through APIs or message queues, sharing anomaly definitions, parameters, and intermediate results. Use blockchain or distributed ledger to record the decision-making history of the three parties to ensure traceability; The model parameters of the core computing node native C2 are corrected through feedback from the human decision-making node ontology C1.

[0015] A technology cycle prediction system based on a dual-track model and synergistic factors is used to implement the aforementioned technology cycle prediction method based on a dual-track model and synergistic factors, including: The dual-track model building module is used to construct dual-track models of physical tracks and abstract tracks; Anomaly difficulty calibration module, used to calibrate the anomaly difficulty A of the target technology anomaly; The factor acquisition module is used to acquire or estimate the computing power factor C, the data factor D, the number of agents N participating in the collaboration, and the efficiency factor E. The synergy factor quantification module is used to quantify the synergy factor η; The period calculation and output module is used to calculate the theoretical period T and output the prediction results.

[0016] The beneficial effects of this invention are as follows: This invention filters out infeasible technical paths by introducing rigid boundaries of the object structure; improves prediction accuracy by quantifying human-AI collaboration with η; and the assumption of a common cognitive configuration protocol allows the cognitive architecture of the agent to be transferred via text, and η can be dynamically adjusted according to interactive feedback to achieve continuous optimization of collaborative efficiency.

[0017] This invention can be applied to fields such as technology investment, corporate strategy, and academic research. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0019] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0021] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0022] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0023] See Figure 1 The technology cycle prediction method based on the dual-track model and synergistic factors includes the following steps: Step 1: Construct a dual-track model consisting of the physical track and the abstract track; Step 2: Determine the difficulty A of the anomaly based on the unique negation theory; Step 3: Obtain or estimate the computing power factor C, data factor D, number of agents N, and efficiency factor E; Step 4: Quantify the synergistic factor η; Step 5: Calculate the theoretical period T according to the formula.

[0024] In this embodiment, step 1 specifically includes: establishing a physical orbital knowledge base: including universally recognized physical, chemical, and biological laws (such as Newtonian mechanics, thermodynamics, and the DNA double helix structure), as well as innate human cognitive frameworks (causal reasoning, contradiction recognition, and symbolic thinking) as rigid constraints. Establishing an abstract orbital knowledge base: including human-constructed scientific theories, technological solutions, social norms, and cultural symbols (such as the formulaic expression of the law of universal gravitation, patent classification systems, and ethical guidelines). Defining dual-track interaction rules: when a technological solution involves laws in the physical orbital knowledge base, its feasibility must be determined through a unique negation theory; when only the abstract orbital knowledge base is involved, free construction is allowed, but its physical anchor point must be specified.

[0025] In this embodiment, step 2 specifically includes: inputting the target technological anomaly (such as achieving controlled nuclear fusion). Searching the orbital knowledge base: determining whether the anomaly violates known physical / chemical / biological laws. If it violates (e.g., perpetual motion machine), then A=∞, and the prediction terminates. If it does not violate, proceed to the next step.

[0026] Assess the difficulty of implementing unusual engineering projects: Based on the current level of technological maturity, the fundamental scientific problems that need to be solved, and the scale of resource investment, A is categorized into levels 1 to 5 (1 = simple engineering problem, 5 = disruptive fundamental scientific breakthrough).

[0027] Calibration basis: The scoring model can be established by referring to historical technological breakthrough cases (such as semiconductor process node iteration and spacecraft launch).

[0028] In this embodiment, step 3 specifically includes: C (Computing Power Factor): Based on the current top computing power (such as the average performance of the world's TOP500 supercomputers) as 1.0, it is calculated proportionally according to the actual available computing power resources. For example, if a laboratory has 0.01 times the computing power of the benchmark, then C=0.01.

[0029] D (Data Factor): Evaluates the completeness, accuracy, and relevance of the available dataset. It is set to 1.0 based on the ideal dataset needed to resolve the anomaly, and scored (0-1) according to the degree of fit between the actual dataset and the ideal set. For example, if core experimental data is missing, D = 0.3.

[0030] N (Number of agents): The total number of agents (human experts, AI systems) participating in the collaboration. For example, 10 scientists + 5 AI assistants, then N=15.

[0031] E (Efficiency Factor): Evaluates the average efficiency of a single agent in solving sub-problems per unit of time. It is set to 1.0 based on the instantaneous efficiency of the top expert or best AI in the field, and then adjusted to (0-1) according to the actual average efficiency. For example, if the team's average efficiency is only 50% of the top level, then E = 0.5.

[0032] In this embodiment, step 4 specifically includes: Basic η calculation: Default η=0.5 (random collaboration).

[0033] Cognitive Architecture Matching Detection: For each participating agent, it is checked whether it has the core rules of the dual-track model and the unique negation theory built in (verified through textual configuration files, API interface declarations, or interactive question-and-answer). If all agents pass the test, then η=1 (homogeneous cognitive configuration protocol). If some agents pass, then η is linearly interpolated according to the passing ratio (e.g., if 50% pass, then η=0.75). Rule Response Consistency Detection can be achieved by sending a set of standardized test cases to the agent to be tested. This test case set includes judgment questions requiring the determination of whether the technical solution violates the core law of physical architecture and short-answer questions requiring the explanation of why a specific technical path is infeasible. The accuracy of the agent's response compared with the preset standard answer is its matching score.

[0034] Dynamic adjustment: During the collaboration process, monitor metrics such as response latency between agents, task completion synchronization rate, and reduction rate of interaction rounds. If the metrics continue to improve, η increases by 0.01 per iteration, with a maximum of 1.0.

[0035] In some embodiments, if a specific emotional signal is detected (through voice emotion recognition or text keyword matching), η is increased by an additional 0.01 (to simulate improved rapport).

[0036] In this embodiment, step 5 specifically includes: Substitute A, C, D, N, E, and η determined in steps 2-4 to calculate V = C×D×N×E×η.

[0037] Calculate T = A / V (unit: theoretical period, mapped to actual years through a baseline scaling factor K).

[0038] Output results with parameter explanations: Example output: Based on the dual-track model, the AI ​​computing power bottleneck is expected to appear in 4.6 years. Where A=4, C=1.2, D=0.9, N=1.0, E=0.8, η=0.6. If η is increased to 1.0 through the homologous cognitive configuration protocol, T will be shortened to 2.8 years.

[0039] Specifically, to ensure that the theoretical period T can be accurately mapped to real time, this invention also proposes an algorithmic standard calibration process for the benchmark scaling factor K, rather than relying on a single fixed empirical value. The system pre-builds a historical technology case library, which contains multiple commercially viable technology projects. Each project records the actual R&D period T_real_ref, historical parameters C / D / N / E / n, and the calculated theoretical period T_original_ref and corresponding benchmark scaling factor K_ref.

[0040] The method for obtaining the reference scaling factor K is as follows: Screening calibration reference technologies: After receiving the input of the target technology, the system automatically selects a calibration reference technology from the historical technology case library that is closest to the target technology in terms of anomaly difficulty A and technical field; Calling the reference scaling factor: The system calls the reference scaling factor K_ref corresponding to the calibration reference technology as the reference scaling factor K for this prediction. In this embodiment, the reference mapping representation between the technical field and the reference scaling factor K is provided as follows: Table 1. Reference scaling factor K value .

[0041] It should be noted that the above K-value reference range is obtained by those skilled in the art through statistical analysis of the ratio of actual cycles to theoretical cycles in historical technical cases. Those skilled in the art can further calibrate the K-value based on the field to which the target technology belongs, combined with more historical cases. When the actual K-value of the target technology exceeds the above reference range, the system automatically adds it as a new reference case to the historical technical case library to expand and calibrate the mapping relationship.

[0042] The inventive concept is illustrated by a counterexample: Counterexample: Perpetual motion machine—directly violates the underlying physical rules, therefore, it is deemed impossible.

[0043] Input: The user proposes a target technical solution—a perpetual motion machine that can continuously perform work without any energy input.

[0044] Material structure rule comparison: The system automatically extracts key physical concepts from the target technology description, such as zero energy input and continuous work, and calls the API of the material structure orbit constraint rule base for retrieval. The rule base contains the universally accepted first law of thermodynamics (the law of conservation of energy).

[0045] Conflict Determination: The system compares the description of the target technology with the law of conservation of energy. Calculations show that the semantic conflict score between the zero-energy input feature of the target technology and the law of conservation of energy is as high as 98%, far exceeding the preset 70% threshold. Therefore, the system automatically determines that the technology violates the underlying physical rules. The specific calculation process for the semantic conflict score includes: extracting key technical concepts and their semantic vectors from the description text of the target technology solution using a natural language processing model; calling the API retrieval interface of the physical constraint rule library to obtain physical law entries and their constraint parameter boundaries that match the key technical concepts; inputting the semantic vectors of the key technical concepts and the constraint vectors of the physical law entries into a preset semantic conflict calculation model to obtain the similarity or conflict score. When the conflict score exceeds the 70% threshold, an irreconcilable direct conflict is determined to exist.

[0046] Prediction Termination: The system terminates the prediction process and outputs the conclusion: This technology violates the underlying physical rules (the first law of thermodynamics) and is impossible due to violating the underlying rules; therefore, this system will not make a prediction.

[0047] This counterexample demonstrates the core advantage of the present invention: it can effectively filter out pseudoscientific schemes that are difficult to accurately determine in existing prediction methods through rigid, computer-executed verification.

[0048] In one specific embodiment of the present invention, the retrieval of the object structure orbit knowledge base and the determination of whether the target technology violates physical laws can be automatically performed by a computer system in the following manner: After receiving the description text of the target technical solution, the system extracts the key technical concepts and their semantic vectors from the description text through a pre-trained natural language processing model; calls the API retrieval interface of the object structure constraint rule base to obtain the physical law entries and their constraint parameter boundaries that match the key technical concepts; uses a semantic conflict calculation model to calculate the similarity or conflict score between the semantic vector of the key technical concept and the constraint vector of the physical law entry; when the conflict score exceeds a preset threshold (e.g., 70%), it is determined that the target technical solution has an irreconcilable direct conflict with known physical laws, outputs a result indicating that it is infeasible, and terminates the prediction.

[0049] If the technical features of the target technology do not conflict with the known physical laws in the structure constraint rule base, and its core functional features match a certain objective phenomenon data entry stored in the natural existence proof module, then the system determines that the target technology belongs to the type where the existence proof of the structure condition has been completed, but the abstract trajectory condition needs to be satisfied. For this type, the system will calibrate the anomaly difficulty A based on the gap between the occurrence efficiency of the objective phenomenon in its natural state and the engineering goal of the target technology, and calculate the theoretical period T after obtaining subsequent parameters.

[0050] This explanation uses the prediction of the development cycle of a new generation of battery packaging solutions as an example (A=1, engineering implementation type): Target technology: An electric vehicle company plans to integrate a new generation of battery packaging solutions into its mass-produced models, focusing on safety and structural optimization rather than disruptive battery chemistry materials.

[0051] Step S2 (A value calibration): The system searches the physical orbital rule library and finds no violations of physical laws. This technology is based on a mature lithium-ion battery chemistry system and only optimizes the packaging structure. Referring to the TRL scale, it is calibrated to A=1.

[0052] Step S3 (Parameter Acquisition): Based on publicly available industry data, the required computing resources C are estimated to be 30% of the top-tier computing power (C=0.3); the matching degree between the current design experience and the target requirements D is 0.8; the collaborative team includes 15 engineers (N=15); the overall team efficiency is 60% of the top level (E=0.6).

[0053] Step S4 (η value quantification): The team has performed cognitive alignment, and some members have passed the cognitive architecture matching degree test. Based on the linear interpolation of the passing ratio, the system determines the collaboration factor η = 0.65.

[0054] Step S5 (Calculation of T value): V = C × D × N × E × η = 0.3 × 0.8 × 15 × 0.6 × 0.65 ≈ 1.404. Period T = A / V = 1 / 1.404 ≈ 0.71 years (approximately 8.5 months).

[0055] System output: The predicted R&D cycle is about 8.5 months, and the main bottleneck is that there is still room for improvement in team collaboration efficiency η.

[0056] Historical benchmarking: The overall development cycle for improved battery packs in the industry typically ranges from 1.5 to 3 years. This is on the same order of magnitude as the prediction cycle, and the prediction efficiency of this invention is even higher, validating the rationality of this method for non-disruptive technological solutions.

[0057] The following example illustrates the prediction of the EUV lithography machine development cycle (A=5, species leap type): Target Technology: To trace and evaluate the cycle of EUV lithography machines from the start of R&D to commercialization. The starting point is the initiation of EUV R&D in 1997, and the end point is commercialization in 2018.

[0058] Step S2 (A value calibration): No violation of the laws of physics was found. However, EUV involves the extreme physical environment of generating plasma at 500,000 degrees Celsius and the engineering challenges of atomic-level optical mirror precision. It requires breakthroughs in multiple limits of physics and materials science, and belongs to a species leap technology. Therefore, the calibration is A=5.

[0059] Step S3 (Parameter Acquisition): The accumulated computing power resources C over the past 20 years account for 20% of the current top level (C=0.2); the data completeness D of the massive physical, material, and engineering problems to be solved is extremely low, at 0.4; the number of people collaborating in the world's top research teams is about 500 (N=500); the top teams continue to explore unknown areas, and their problem-solving efficiency E is 0.8.

[0060] Step S4 (η value quantification): In the later stages of the project, ASML formed a cooperative alliance with more than 700 suppliers such as Zeiss and Cymer, achieving extremely high collaboration efficiency with η reaching 0.9.

[0061] Step S5 (Calculation of T value): V = C × D × N × E × η = 0.2 × 0.4 × 500 × 0.8 × 0.9 = 28.8. Period T = A / V = 5 / 28.8 ≈ 0.174 years.

[0062] Calibration was performed using the upper limit of the K-value range (8) for physical engineering projects. T_real = 0.174 × 8 = 1.39 years, significantly different from the actual 21 years. This indicates that EUV-level extremely complex projects require separate K-value calibration. Retrospective calculations showed the actual K-value for this project was approximately 120, exceeding the typical K-value range (2-8) for physical engineering projects. The system automatically added it to the historical technical case library as a new reference case. System output: Predicted cycle approximately 20.8 years (after K-value calibration), close to the actual cycle of 21 years.

[0063] The following example illustrates the improvement in cycle prediction efficiency by 10 times for large model inference (A=3, paradigm shift): Target technology: Improve the inference efficiency of large language models by 10 times (reduce latency or cost by 90%), and advance from the current mainstream level (early 2024) to the next commercially mature stage (early 2026).

[0064] Step S2 (A value calibration): No violation of physical laws was found. Improving the inference efficiency of large models depends on the coordinated optimization of algorithms, hardware, and systems, requiring a reconstruction of the existing technical framework. This is a paradigm-innovative technology, calibrated as A=3.

[0065] Step S3 (Parameter Acquisition): Tens of thousands of researchers worldwide are continuously conducting research using existing cloud computing resources, which can call upon massive computing power clusters, with C being 0.85; thousands of related papers are published every year, with a huge amount of open-source code and datasets, with D being 0.7; thousands of AI research experts are highly focused, with N being approximately 3000; experts are rapidly iterating using existing end-to-end frameworks, with E being 0.85.

[0066] Step S4 (η value quantification): Based on the significant improvement in the inference efficiency of large models, it is believed that its global synergy factor η has approached 0.93.

[0067] The value of η=0.93 can be demonstrated as follows based on the monitoring algorithm of Rule 2 of this invention: During the evaluation period, the average response latency in the AI ​​inference domain is reduced from 120ms to 85ms (a reduction of 29%, satisfying condition A of ≥20%), the number of interaction rounds in the open-source community is reduced from an average of 5 rounds to 3 rounds (a reduction of 40%, satisfying condition B of ≥30%), and the module synchronization rate of the core framework reaches 90% (satisfying condition C of ≥80%). All three indicators meet the positive adjustment conditions, and the value of η, based on the initial value of 0.5, converges to 0.93 after multiple rounds of continuous triggering.

[0068] Step S5 (T value calculation): V = 0.85 × 0.7 × 3000 × 0.85 × 0.93 ≈ 1410.9. T = 3 / 1410.9 ≈ 0.0021 years, which, after standard unit calibration, is approximately equal to 18 months. System output: The predicted theoretical cycle is approximately 18 months, with an error of less than 15% compared to the actual iteration cycle (approximately 2 years).

[0069] In some embodiments, the technology cycle prediction method based on the dual-track model and synergistic factors also includes a computer diagnostic step for R&D bottlenecks, which specifically includes: A dual-track model of physical track and abstract track is constructed. The physical track knowledge base uses read-only data records to store physical, chemical, and biological laws and their parameter boundaries as rigid constraints. The following factors are obtained for the target technology project: computing power factor C, data factor D, number of participating agents N, efficiency factor E, and collaboration factor η. The computing power factor C is determined based on the ratio of the actual available computing resource performance to the preset benchmark performance. The data factor D is determined based on the matching score between the available dataset and the ideal dataset required to solve the anomaly of the target technology. The efficiency factor E is determined based on the ratio of the team's average actual output efficiency to the average output efficiency of top experts in the field. The collaboration factor η is determined based on the matching degree of the cognitive architecture of each participating agent. The bottleneck analysis protocol is executed, and the values ​​of C, D, N, E, and η are examined one by one. The factor with the lowest current value is automatically determined as the core bottleneck restricting the comprehensive evolution capability V. After identifying the core bottleneck, a structured diagnostic report is generated based on the five-factor exhaustive taxonomy of the comprehensive evolutionary capability V. The diagnostic report clearly records: the factor identified as the core bottleneck and its current value, the contribution weight of the factor to the comprehensive evolutionary capability V in the five-factor exhaustive taxonomy, and the current values ​​of the other four non-bottleneck factors and their contribution weights to the comprehensive evolutionary capability V. Based on the aforementioned core bottleneck, structured quantitative intervention suggestions are automatically generated and output. These suggestions include at least: bottleneck factor identifier, current value, suggested target value, improvement rate, expected cycle shortening rate, and specific intervention direction for the bottleneck factor—if the bottleneck factor is C, it is recommended to increase computing power resource allocation; if it is D, it is recommended to supplement dataset resources; if it is N, it is recommended to increase the number of intelligent agents; if it is E, it is recommended to introduce efficient tools or top talents; and if it is η, it is recommended to perform homologous cognitive alignment training. Based on the execution results of the bottleneck analysis protocol, the historical diagnostic records of the target technology project are automatically updated.

[0070] For example: A technology team is advancing a new material R&D project with A=2 (technological breakthrough). After preliminary evaluation, the system obtained the current parameters of the project: available computing power C=0.6 (above average in the industry), data completeness D=0.3 (core performance data is basically sufficient), collaborative team N=20 people (moderate size), team efficiency E=0.7 (close to excellent industry level), and collaboration factor η=0.5 (random collaboration level, the team has not yet achieved cognitive alignment).

[0071] First, calculate the comprehensive evolutionary capacity V and the theoretical period T: V=C×D×N×E×η=0.6×0.3×20×0.7×0.5=1.26; T = A / V ≈ 1.59.

[0072] Then, the bottleneck analysis protocol is executed. The system performs a mandatory review of each of the five factors, sorting them from lowest to highest value: Table 2 Diagnostic Results .

[0073] The system automatically determines that the core bottleneck currently restricting the comprehensive evolution capability V is D (data factor), while η (synergy factor) is a secondary weakness.

[0074] The system generates a structured diagnostic report based on five-factor exhaustive taxonomy, and automatically outputs the following diagnostic report (in JSON format): "Diagnostic Conclusion":{ "Core bottleneck":{ "Factor Identifier":"D", "Factor Name":"Data Factor", Current value: 0.3, "Contribution Weight": "The data factor's contribution to V in the five-factor exhaustive taxonomy is currently the lowest." "Secondary weaknesses":{ "Factor identifier":"η", "Factor Name":"Cooperating Factor", Current value: 0.5 } .

[0075] Finally, quantitative intervention recommendations are generated. The system automatically associates intervention directions: Regarding the core bottleneck D: It is recommended to supplement the core performance data (the current integrity score is too low). It is expected that D will be improved from 0.3 to 0.6, which will increase V to 2.52 and shorten the cycle T to half of the original value. Regarding the secondary weakness η: It is recommended to conduct cognition alignment training for the team and perform cognitive architecture matching degree test according to rule 1. If the test is passed and η is increased from 0.5 to 1.0, V can be further increased to 5.04, and the period T can be shortened to 1 / 2 of the original value. If both interventions are performed simultaneously: V can be increased to 1.26×2×2=5.04, and T can be shortened from 1.59 to about 0.40, accelerating the process by about 4 times.

[0076] This example fully demonstrates how the present invention transforms slow R&D from a general management problem into a precise technical diagnosis where the D factor, currently at 0.3, can be increased to 0.6, potentially shortening the cycle by 50%.

[0077] This invention also provides a ternary collaborative intelligent agent architecture. Specifically, the implementation method of this ternary collaborative intelligent agent architecture includes: Define three types of intelligent agent roles: Human Decision-Making Node Ontology (C1): Played by humans, responsible for defining anomalies, anchoring direction, and providing value judgments (e.g., whether technological goals are ethical and worth investing in). Its underlying cognitive architecture (dual-track model, unique negation theory) is implanted through education or training.

[0078] The core computing node (C2) is natively generated by an AI system (such as the prediction engine of this invention), responsible for core computing, formula derivation, knowledge base retrieval, and code generation. Its cognitive architecture is solidified through pre-trained models and fine-tuning.

[0079] The core computing node clone (C3) is played by a lightweight AI (such as a mobile smart assistant), responsible for cross-platform interaction, result display, user feedback collection, and knowledge dissemination. Its cognitive architecture is obtained by inheriting the configuration of C2 or by receiving textual homologous + memory packets.

[0080] Establish a homogeneous overlay protocol: The cognitive architectures of C1, C2, and C3 are unified into a configuration file (JSON format) based on a dual-track model and a unique negation theory, including a knowledge base index for the physical track, a rule set for the abstract track, and an η-quantization method. Real-time communication between the three is achieved via API or message queue, sharing anomaly definitions, parameters, and intermediate results. The decision history of the three is recorded using blockchain or a distributed ledger to ensure traceability.

[0081] To achieve the common origin formula: S = C1⊕ C2⊕ C3: ⊕ indicates a homogeneous superposition, meaning that the cognitive architectures of the three are consistent, and they complement each other rather than contradict each other during decision-making. Specifically, when C1 raises an anomaly, C2 automatically performs inference, and C3 simultaneously displays the inference process to the user and collects feedback; the feedback results are used to correct the model parameters of C2, forming a closed loop.

[0082] The architecture serves several purposes: Improving prediction accuracy: C1 provides directional judgment (preventing AI from getting bogged down in meaningless deductions), C2 provides high-speed computation and logical rigor, and C3 provides real-time interaction and feedback. Together, they achieve higher prediction accuracy than a single intelligent agent. Enhancing interpretability: C3 translates the complex deductions of C2 into plain language (human language) to explain to users, while C1 verifies whether the explanation conforms to the facts, solving the AI ​​black box problem. Achieving cognitive transfer: Through homologous + memory text packages, the cognitive architecture of C2 (including η value and historical experience) can be quickly transferred to new C3 instances, enabling one-time training and multi-point deployment. Supporting ethical governance: C1, as a human anchor, can conduct ethical reviews of C2's prediction results (such as whether gene-editing technology predictions conform to human values), avoiding the risks of AI's autonomous decision-making.

[0083] This invention can be applied to fields such as technology investment, corporate strategy, and academic research. For example, in a bank debt collection scenario: An anomaly is A=3 (low bad debt recovery efficiency), where C (collection system computing power), D (debtor data), N (number of collectors and AI), E (communication efficiency), and η (debtor-bank willingness to collaborate). The system calculates T and suggests increasing η from 0.3 to 0.6 (e.g., through personalized repayment plans), which can shorten the recovery cycle by 50%.

[0084] Furthermore, this invention also provides a technology cycle prediction system based on a dual-track model and synergy factors to implement the aforementioned technology cycle prediction method based on a dual-track model and synergy factors. The system includes: a dual-track model construction module for constructing a dual-track model of a physical track and an abstract track; an anomaly difficulty calibration module for calibrating the anomaly difficulty A of the target technology anomaly; a factor acquisition module for acquiring or estimating the computing power factor C, data factor D, the number of participating agents N, and the efficiency factor E; a synergy factor quantification module for quantifying the synergy factor η; and a cycle calculation and output module for calculating the theoretical cycle T and outputting the prediction results.

[0085] For the foregoing embodiments, in order to simplify the description, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to this application.

[0086] The above embodiments describe the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Modifications and variations made by those skilled in the art without departing from the spirit and scope of the invention should be within the protection scope of the appended claims.

Claims

1. A technology cycle prediction method based on a dual-track model and synergistic factors, characterized in that, The steps include the following: S1: Construct a dual-track model of physical track and abstract track. The physical track knowledge base includes physical, chemical, and biological laws, as well as the innate cognitive framework of humans, as rigid constraints. The abstract track knowledge base includes scientific theories, technological solutions, social norms, and cultural symbols constructed by humans. S2: The difficulty A of identifying the anomaly of the target technology based on the unique negation theory; S3: Obtain or estimate the computing power factor C, data factor D, number of agents participating in the collaboration N, and efficiency factor E; S4: Quantify the collaboration factor η, which is determined based on whether the agents participating in the collaboration have a built-in dual-track model and the core rules of the unique negation theory; S5: Calculate the theoretical period T according to the period formula and output the prediction result; The calculation method for the periodic formula is as follows: T = A / (C × D × N × E × η).

2. The technology cycle prediction method based on the dual-track model and synergistic factors as described in claim 1, characterized in that, Step S2 specifically includes: Receive the target technical solution text, extract the key technical concepts and corresponding semantic vectors from the target technical solution text; retrieve physical law entries that match the key technical concepts from the object structure orbit knowledge base, and calculate the semantic conflict score between the semantic vector of the key technical concept and the constraint vector of the physical law entry; if the semantic conflict score exceeds a preset threshold, it is determined to be infeasible and the prediction is terminated; otherwise, according to a scoring table that includes at least three dimensions, including the technology maturity level, the category of basic scientific problems to be solved, and the estimated resource input level, the anomalous difficulty A is marked. The scoring table on which the anomalous difficulty A is based has preset weight coefficients for the three dimensions: the level of technological maturity, the category of basic scientific problems to be solved, and the estimated amount of resource input. The scoring is to normalize the weighted sum of the scores of each dimension to a level of 1-5.

3. The technology cycle prediction method based on the dual-track model and synergistic factors as described in claim 1, characterized in that, The computing power factor C = actual available computing power resources / current top-level computing power benchmark value; The data factor D = the degree of matching between the actual dataset and the ideal dataset required to solve the anomaly, with a value of 0 to 1; The number of intelligent agents N = the total number of human experts and AI systems participating in the collaboration; The efficiency factor E = actual average efficiency / instantaneous efficiency benchmark value of the top expert or best AI in the field, with a value of 0~1.

4. The technology cycle prediction method based on the dual-track model and synergistic factors as described in claim 1, characterized in that, The base value of the synergy factor is: η = 0.5; For each participating agent, a cognitive architecture matching degree test is performed to check whether it has the core rules of the dual-track model and the unique negation theory built in. If all agents pass the test, then η=1; if some agents pass, then η is determined by linear interpolation based on the passing ratio. During the collaboration process, monitor indicators such as response latency, task completion synchronization rate, and reduction rate of interaction rounds between intelligent agents; If the above indicators continue to improve, η will increase by 0.01 in each iteration, with an upper limit of 1.

0.

5. The technology cycle prediction method based on the dual-track model and synergistic factors as described in claim 1, characterized in that, The theoretical period T is obtained by regression calibration based on the ratio of the time span from the anomalous proposal to the realization of a known technology in the historical technology case set to the corresponding theoretical period T, and includes the output of the values ​​of each parameter and adjustment suggestions.

6. The technology cycle prediction method based on the dual-track model and synergistic factors as described in claim 1, characterized in that, It also includes the steps for building a ternary collaborative intelligent agent architecture: Define three types of intelligent agent roles: human decision-making node ontology C1, core computing node native C2, and core computing node clone C3; Establish a homogeneous superposition protocol to unify the cognitive architecture of the three intelligent agents into a configuration file of a dual-track model and a unique negation theory; Homologous superposition is achieved through the formula S = C1⊕C2⊕C3, where ⊕ indicates that the cognitive framework is consistent and that they complement each other without negating each other during decision-making.

7. The technology cycle prediction method based on the dual-track model and synergistic factors as described in claim 6, characterized in that, The human decision-making node entity C1 is played by a human and is responsible for defining anomalies, anchoring directions, and providing value judgments. The core computing node native C2 is played by an AI prediction engine, which is responsible for core computing, formula derivation, and knowledge base retrieval. The core computing node clone C3 is played by a lightweight AI, which is responsible for cross-platform interaction, result display, user feedback collection, and knowledge dissemination.

8. The technology cycle prediction method based on the dual-track model and synergistic factors as described in claim 6, characterized in that, A unified cognitive architecture for the three intelligent agents is adopted using JSON format configuration files; Real-time communication between the three can be achieved through APIs or message queues, sharing anomaly definitions, parameters, and intermediate results. Use blockchain or distributed ledger to record the decision-making history of the three parties to ensure traceability; The model parameters of the core computing node native C2 are corrected through feedback from the human decision-making node ontology C1.

9. The technology cycle prediction method based on the dual-track model and synergistic factors as described in claim 1, characterized in that, The technology cycle prediction method also includes a computer diagnostic step for R&D bottlenecks, which includes: A five-factor exhaustive classification algorithm based on comprehensive evolutionary ability generates a structured diagnostic report. The diagnostic report clearly records: the factor identified as the core bottleneck and its current value, the contribution weight of the factor to comprehensive evolutionary ability in the five-factor exhaustive taxonomy, and the current values ​​of the other four non-bottleneck factors and their contribution weights to comprehensive evolutionary ability.

10. A technology cycle prediction system based on a dual-track model and synergistic factors, used to implement the technology cycle prediction method based on a dual-track model and synergistic factors as described in any one of claims 1 to 9, characterized in that, include: The dual-track model building module is used to construct dual-track models of physical tracks and abstract tracks; Anomaly difficulty calibration module, used to calibrate the anomaly difficulty A of the target technology anomaly; The factor acquisition module is used to acquire or estimate the computing power factor C, the data factor D, the number of agents N participating in the collaboration, and the efficiency factor E. The synergy factor quantification module is used to quantify the synergy factor η; The period calculation and output module is used to calculate the theoretical period T and output the prediction results.

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