Innovation resource self-organizing scheduling method based on ant colony cooperation and bayesian learning

Abstract

The present application relates to resource scheduling technical field, specifically to the innovative resource self-organizing scheduling method based on ant colony cooperation and bayesian learning, including the following steps: S1, factor space state variable initialization; S2, structured reading of public clues; S3, probabilistic inference of opportunity credibility.The present application combines the decision-making and collaborative behavior of individual innovation subjects, uses dynamically updated public clues, opportunity credibility inference, subjective value evaluation and dynamic depiction of collaboration threshold, effectively optimizes resource allocation and collaboration mode, improves decision-making efficiency through bayesian inference in practical application, reduces the negative impact of information overload, and ensures efficient collaboration when the project scale expands through the introduction of synergistic effect and immediate adjustment mechanism, enhances the adaptability and continuous optimization ability of the system through the endogenous decay of feedback mechanism and environmental memory, thereby improving the utilization efficiency of innovative resources and the success rate of project execution.

Description

Technical Field

[0001] This invention relates to the field of resource scheduling technology, specifically to an innovative resource self-organizing scheduling method based on ant colony collaboration and Bayesian learning. Background Technology

[0002] Existing innovation resource scheduling methods mainly rely on traditional optimization algorithms or static decision models, which often ignore the dynamic interaction and collaborative effects among innovation subjects. Many methods adopt static resource allocation strategies, which cannot effectively handle the allocation and redistribution of resources among multiple subjects. Most existing methods fail to fully consider the individual differences of innovation subjects and the nonlinear characteristics of collaboration, resulting in the inability to achieve optimal resource scheduling in complex environments. When faced with information overload and uncertainty, traditional scheduling methods often rely on simplifying assumptions, ignoring the timeliness of information updates and the dynamic changes in their impact on decision-making, making it difficult to adapt to rapidly changing innovation needs in practical applications.

[0003] Current scheduling methods often fail to effectively address information overload when dealing with large-scale innovation projects. Innovators frequently face decision-making difficulties when confronted with vast amounts of complex information. Traditional methods typically employ static decision thresholds or fixed rules for screening, lacking dynamic adaptability. Existing technologies neglect the non-linear effects of collaborative behavior, failing to flexibly adjust based on the number of participants or the complexity of collaboration. While some methods attempt to optimize decisions by incorporating historical data, they often rely too heavily on prior information and cannot reflect real-time changes, leading to reduced efficiency and accuracy. Existing methods struggle to balance project costs and benefits, often employing fixed collaboration cost structures that fail to dynamically adjust based on actual participation, historical experience, and individual capabilities, thus hindering optimal resource allocation in practice. Therefore, we propose a self-organizing scheduling method for innovation resources based on ant colony collaboration and Bayesian learning. Summary of the Invention

[0004] In view of the above-mentioned shortcomings of the existing technology, the first objective of the present invention is to provide an innovative resource self-organizing scheduling method based on ant colony collaboration and Bayesian learning, so as to solve the problems in the background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution: An innovative resource self-organizing scheduling method based on ant colony collaboration and Bayesian learning includes the following steps: S1. Initialize the spatial state variables of the feature; S2, Structured reading of public clues; S3, Probabilistic Inference of Opportunity Credibility; S4. The individualized generation of subjective value; S5, Dynamic depiction of collaboration thresholds; S6. Optimal term for investment return ratio is locked in; S7. Execution of the action grouping process; S8. Endogenous incremental decay of environmental memory; S9, Closed-loop iterative structural evolution control.

[0006] The present invention is further configured such that: in step S1, during the initialization of the feature space state variables: S1.1 Defining the set of innovation subjects With project collection And configure heterogeneous parameters for each subject ( Characterizes its weight for different information sources. (characterizing its sensitivity to public clues), while configuring inherent appeal for each project. Collaboration threshold with benchmarks This ensures that all subsequent decisions, learning, and interactions have clear variable carriers; S1.2 Define a common intensity that varies over time for each project. and given a historical baseline These quantities will be incorporated into the subsequent probability inference formula. The main body is not judged in a vacuum, but based on... Historical accumulation and The judgment is based on real-time fluctuations; S1.3, Define cumulative experience and the number of participants in real time This allows us to characterize both "lower threshold with accumulation" in the cost function and "lower efficiency with congestion" in the correction term; S1.4, Define the capability status for the subject. And set the attention filtering threshold. Minimum acceptable profit threshold These thresholds will be used for tiered filtering and subsequent feedback triggering criteria.

[0007] The present invention is further configured such that: in step S2, the structured reading of common clues: S2.1, at time Constructing an observation set for the main body Furthermore, a moving average or time window can be added to the implementation layer (nearest). To reduce noise, the average value of each period is used. It does not fluctuate drastically due to accidental contributions, making it easy to perform stable probability calculations; S2.2, will Normalization or logarithmic compression (commonly used in implementation) However, in theoretical expression it is still based on Entering the likelihood form; S2.3, Building a long-standing reputation Current popularity Intrinsic attributes of the project The evidence is clearly defined as three channels, and all are uniformly incorporated into a Bayesian synthesis probability formula; S2.4, Subject-specific parameters As an exponential weight applied to and Up, and As a response The sensitivity coefficient.

[0008] The present invention is further configured such that: in step S3, the probabilistic inference of chance confidence: S3.1. In the absence of real-time information or during periods of information scarcity, an initial ranking can still be formed based on historical consensus and inherent attractiveness, yielding the following prior probabilities: When implementing, it can be... , Rewritten as k, k is used to express individual differences; S3.2. Interpreting "current public strength" as the strength of evidence for the hypothesis that "the project is indeed worth investing in," we use a likelihood function in the following form: in The amplification factor of the heat difference by the control subject; The larger the size, the easier it is for the main body to concentrate on a few hot spots in the early stages; the smaller the size, the more exploratory it retains. S3.3. Combine prior knowledge and likelihood to form "the strength of belief in the value of the project at the current moment." According to Bayes' theorem, this is achieved by observing evidence... Afterwards, the subject responded to the hypothesis. The belief is updated to the posterior probability: The purpose of this step is not to directly decide "to do it", but to compress the massive number of options into a set of probability weights, so that subsequent rational screening is computationally feasible. S3.4. Use a threshold to concentrate the subject's evaluation resources on a few alternatives, simulating attention allocation under information overload, and obtain the Bayesian posterior probability of each item. Subsequently, the innovation entities do not directly select randomly based on this probability, but first conduct a rational screening. A threshold for attention is set. A set of candidate projects worthy of further value assessment was selected: in The set of all innovative projects in the system, with a posterior probability threshold. The introduction of this means that the subject may miss those projects that have "weak signals but high actual value," which is the price of bounded rational decision-making: in an environment of information overload, the subject must sacrifice some exploration possibilities in exchange for decision-making efficiency.

[0009] The present invention is further configured such that: in step S4, the individualized generation of subjective value: S4.1, The objective attractiveness of the project Transforming this into "expected value from the perspective of a particular subject," we introduce cognitive differences: in For the project Its inherent appeal (objective value). as the main body For the project The subjective perception deviation coefficient; S4.2. To ensure that biases have a statistical structure rather than random noise, making them analyzable and simulable, this paper assumes that, in order to characterize reasonable differences in value judgments among individuals, the following assumptions are made. Follows a log-normal distribution: This setting has the following properties: ① Positiveness: ensuring value perception. ② Mean normalization: group average perception and project intrinsic value ③ Maintain consistency to avoid systematic overestimation or underestimation; ④ Controllable heterogeneity: variance From parameters Complete decision, The larger the value, the stronger the disagreement in judgments among subjects, which can flexibly simulate innovation environments with different degrees of heterogeneity; S4.3, Put Extending from a single scalar to an interpretable structure facilitates more specialized subsequent implementations, allowing for... It comes from a weighted average of multiple indicators (such as technical feasibility, industrial traction, and scientific significance). This can be decomposed into the product of capability mismatch bias and risk preference bias; however, it is ultimately converted back to... Enter your utility formula without compromising the mathematical backbone of the paper's model; S4.4 Clarify that "probability inference" only determines the focus of attention, while "value expectation" enters the profit calculation. Avoid confusing βk with εjk, and strictly distinguish between them: βk appears in... In the middle, represents the weight of ηj by the subject during the attention phase; while Direct change Impact on utility The size of the decision determines the logical "two-stage decision-making" process of the model: screening first, then calculation.

[0010] The present invention is further configured such that: in step S5, the dynamic characterization of the collaboration threshold: S5.1. Define a "starting point" collaboration cost benchmark for each project, as a reference for subsequent reductions. This is a comprehensive measure of the inherent complexity of a project and the initial resource threshold, so that different projects have different levels of difficulty even when there is zero experience. S5.2. Convert the historical accumulation of participating behaviors into state variables that can be invoked by the cost function: : the cutoff time Historically involved in projects The cumulative number of all independent entities, i.e., of which For a moment The number of real-time participants, a variable that represents the "stock of collective experience" accumulated by the project, is the basis for the generation of synergy. S5.3, Formulate the mechanism of "knowledge accumulation lowering the threshold for newcomers", the main body At any moment Participate in the project The required dynamic collaboration costs Defined as: in: : indicates project The baseline collaboration cost refers to the standardized cost that an innovator must pay to independently explore and attempt to solve the core problems of a project when there is no prior collaboration experience (i.e., zero experience stock). This value reflects the inherent technical complexity and initial resource threshold of the project. in ≥0 represents the synergistic effect coefficient; =0, then the cost is constant. If the value is greater than 0, the cost decreases power-lawically with the existing stock of experience. Here, +1 is used to prevent this. Sometimes meaningless divergence occurs; S5.4. Separate "long-term cost reduction" and "short-term congestion" at the variable level to ensure that the interpretation of the cost function is not contaminated when congestion effects are added later. In this step, only allow... rely , without introducing And record in the data structure layer This allows for the use of utility correction terms in the next major step, thus maintaining the "reduce costs first, then correct congestion" modeling approach in your paper.

[0011] The present invention is further configured such that: in step S6, the optimal term for the formation of the input-output ratio is locked: S6.1 Unify value and cost into a "cost-effectiveness" indicator that can be compared across projects. After the entity forms value and cost estimates, it needs to conduct a cost-benefit analysis to form an overall incentive assessment for participating behavior, and define the entity. Participate in the project The unit cost value utility obtained for: The utility function It is the core intrinsic incentive variable driving the subject's subsequent feedback behavior. Its economic meaning is the expected return on investment per unit of cost incurred by the subject, which can be compared to a kind of "return on investment in innovation." A high value indicates that the subject believes (or experiences) the participation behavior has a high "cost-effectiveness," meaning the perceived value significantly outweighs the dynamic collaboration costs incurred, leading to strong satisfaction. Conversely, a low value indicates a lower value. A ratio implies that the expected value is less than the required cost, and participation may be seen as a losing proposition. The reason for using a ratio rather than a difference is that the benchmark collaboration cost varies across different projects. There may be differences in magnitude, making it difficult to compare the "cost-effectiveness" across projects, while the ratio form provides a dimensionless standardized measurement, which is more suitable for horizontal comparisons between different projects. S6.2 Eliminate obviously uneconomical options in advance to reduce noise in subsequent comparisons. The economic meaning of the participation threshold: when... When the expected value is greater than or equal to the participation cost ( The participation behavior has non-negative expected returns, satisfying the basic participation condition of the rational economic agent assumption: S6.3, to characterize the immediate nonlinearity of "too few people leading to insufficient collaboration, too many people leading to communication congestion," and to ensure that the choice reflects the actual state of collaboration, in order to bridge this theoretical gap and enable the model to simultaneously encompass both historical knowledge collaboration and immediate scale effects, the core utility function is optimized by introducing an immediate adjustment factor—the collaboration coefficient. This allows us to construct a more complete actual utility function: Newly added synergy coefficient Specifically designed to characterize the current scale of collaboration The immediate nonlinear impact on individual expected efficacy, with the addition of a synergy coefficient. Specifically designed to characterize the current scale of collaboration The immediate nonlinear impact on individual expected efficacy; S6.4 Transform the "comparison" into explicit action instructions, and output the target projects that the subject needs to invest in during the current period: Within a pool of feasible projects, the entity selects the project that delivers the highest unit cost value to maximize profits.

[0012] The present invention is further configured such that: during step S7, the action grouping process is executed as follows: S7.1 Ensure that "project selection" goes beyond mere intention, generating measurable input records, and at time t, the main body... With the selected project Binding, forming a participating set (i.e., projects participated in during the current period) The main body set) is registered, this step is for subsequent experience storage. The recursion and the scale coefficient The calculations provide a factual basis; S7.2, Specify the condition "whether collaboration is required". To link feedback to project progress and avoid distortion, updates should be made during implementation based on the current challenges of the project and the matching of core capabilities (available capability thresholds or skill vectors). If the main body can independently solve the core problem, then... =1, otherwise =0, this step directly determines whether it is allowed to make a positive contribution to the public strength in the future; S7.3. The cost function is guaranteed to use accumulated experience "up to the present" rather than a static constant, and is updated at the end of the period based on session participation. (Or use cumulative sum as you see fit), thus enabling the next period's... The changes reflect a structure where "the longer the collaboration lasts, the less effort the newcomer has." S7.4. In more specialized implementations, it is necessary to handle situations where the subject switches between projects across multiple cycles, and multiple projects simultaneously compete for the same subject's resources. Resource budgets (time / computing power / attention) should be set for the subject, and it should be constrained that a maximum of one project can be committed to per cycle, or allocated proportionally. Although your paper's main body uses single-choice... While simplified, the engineering implementation can avoid unrealistic "infinite parallel investment" through budget constraints without altering the core formula.

[0013] 9. The innovative resource self-organizing scheduling method based on ant colony collaboration and Bayesian learning according to claim 1, characterized in that, in step S8, the endogenous incremental decay of environmental memory: S8.1 Establish a monotonic mapping between the subject's subjective experience and the increment of public memory, so that public clues can reflect real value. Use the subject's current utility on the selected item as the strength benchmark. When contribution is allowed, the increment is proportional to the utility, providing a summable "contribution" for subsequent public strength updates. S8.2 Ensure that only entities that "still require collaboration and have a positive experience" will positively contribute to public intensity, preventing entities that "have completed / do not require collaboration" from continuing to create false buzz. After participating in a project, entities should conditionally release feedback signals to the environment based on their actual experience and problem-solving status. The rules are as follows: in, This indicates whether the entity possesses the ability to independently resolve the project's current key issues; This is a constant for signal release intensity. A reinforced signal proportional to utility will only be released when the subject needs cooperation and gains a positive experience (utility > 1); if the problem has been solved or the experience is average, it will remain silent. S8.3. Each subject is given a clear incremental calculation formula for easy aggregation. When the above triggering conditions are met, the incremental signal released by the subject is positively correlated with its perceived positive utility, and the functional relationship is defined as follows: in, As the signal release intensity constant, it scales the subjective utility of the subject to the objective contribution to the environmental signal; S8.4. Summarize the increments of all subjects at the project level and add a decay term to simulate attention shift and information obsolescence to avoid permanent locking. At the end of each cycle, perform the following for each project: first decay, then add the increment. At the project granularity, this can be written as: in As the decay rate, this formula unifies the "forgetting mechanism" and the "contribution mechanism" into a single recursive equation, which becomes the input for the next round of inference.

[0014] The present invention is further configured such that: in step S9, the closed-loop iterative structural evolution control: S9.1 Ensure the system strictly iterates in the order of "observation → inference → screening → accounting → action → write-back" to avoid state referencing errors. Perform consistency checks at the loop boundaries: Updates must be completed at the same time level to ensure the next round Using the latest ; S9.2. In the early stages or when information is insufficient, to prevent the system from being locked into suboptimal items too early due to random fluctuations, a small probability random perturbation is added to the selection operator in the implementation, or time annealing is performed on the P threshold (initially a low threshold expands the candidate set, and later the threshold is increased to strengthen the focus). S9.3, When the scale exceeds the optimal collaboration scale At that time, the actual utility of the subject naturally decreases, thus in The emergence of alternative projects drives structural migration and increases the synergy coefficient. Specifically designed to characterize the current scale of collaboration The immediate nonlinear impact on individual expected efficacy, with the addition of a synergy coefficient. Specifically designed to characterize the current scale of collaboration The immediate nonlinear impact on individual expected efficacy is represented by the synergy coefficient function as follows: :project The current number of participants ( ), is a dynamic variable; :project The theoretical optimal scale of collaboration is determined by factors such as project complexity, task decomposability, and the level of collaboration tools. Coordination coefficient, range of values This represents the proportion of individual expected effectiveness achieved at the current scale; S9.4. Provide clear criteria for "when to stop / when to consider stable" for experimental reproduction and engineering deployment, and set termination conditions such as: reaching the maximum cycle time. or If the change is less than the threshold for L consecutive rounds, or if it participates in the distribution... KL divergence convergence, etc.; and define the output index caliber (e.g., per period). Individual Sequence, and The distribution of the data is used to analyze the interpretability and comparability of the "emergent structure" in subsequent analyses.

[0015] Beneficial effects Compared with known public technologies, the technical solution provided by this invention has the following beneficial effects: This invention effectively optimizes resource allocation and collaboration patterns by combining the decision-making and collaborative behaviors of individual innovation agents and utilizing dynamically updated public clues, opportunity credibility inference, subjective value assessment, and dynamic characterization of collaboration thresholds. In practical applications, it improves decision-making efficiency and reduces the negative impact of information overload through Bayesian inference. Furthermore, by introducing synergy and immediate adjustment mechanisms, it ensures efficient collaboration even as project scales up. Through feedback mechanisms and the endogenous decay of environmental memory, it enhances the system's adaptability and continuous optimization capabilities, thereby improving the utilization efficiency of innovation resources and the success rate of project execution. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the innovative resource self-organizing scheduling method based on ant colony collaboration and Bayesian learning proposed in this invention. Figure 2 This is a schematic diagram illustrating the research technical route of the innovative resource self-organizing scheduling method based on ant colony collaboration and Bayesian learning in this invention. Figure 3 This diagram illustrates the growth trend of the number of entities in the BIS "Entity List" in China, based on the innovative resource self-organizing scheduling method of this invention, which is based on ant colony collaboration and Bayesian learning. Figure 4 This is a schematic diagram comparing the trends of R&D expenditure and technology transfer rate in China for the innovative resource self-organization scheduling method based on ant colony collaboration and Bayesian learning, as presented in this invention. Figure 5 This is a schematic diagram illustrating the spatial distribution evolution of the innovation subjects in the self-organizing scheduling method for innovation resources based on ant colony collaboration and Bayesian learning, as described in this invention. Figure 6 This is a time-series diagram illustrating the innovation signal strength of each project in the self-organizing scheduling method for innovative resources based on ant colony collaboration and Bayesian learning, as presented in this invention. Figure 7 This is a schematic diagram of the innovation scale-efficiency relationship curve of the self-organizing scheduling method for innovative resources based on ant colony collaboration and Bayesian learning in this invention. Figure 8 This is a schematic diagram of the scale-efficiency relationship curve at a fine scale for the innovative resource self-organizing scheduling method based on ant colony collaboration and Bayesian learning of this invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0018] The present invention will be further described below with reference to embodiments.

[0019] Example like Figure 1-8 As shown, the innovative resource self-organizing scheduling method based on ant colony collaboration and Bayesian learning includes the following steps: S1. Initialize the spatial state variables of the feature; S2, Structured reading of public clues; S3, Probabilistic Inference of Opportunity Credibility; S4. The individualized generation of subjective value; S5, Dynamic depiction of collaboration thresholds; S6. Optimal term for investment return ratio is locked in; S7. Execution of the action grouping process; S8. Endogenous incremental decay of environmental memory; S9, Closed-loop iterative structural evolution control; In step S1, during the initialization of feature space state variables: S1.1 Defining the set of innovation subjects With project collection And configure heterogeneous parameters for each subject ( Characterizes its weight for different information sources. (characterizing its sensitivity to public clues), while configuring inherent appeal for each project. Collaboration threshold with benchmarks This ensures that all subsequent decisions, learning, and interactions have clear variable carriers; S1.2 Define a common intensity that varies over time for each project. and given a historical baseline These quantities will be incorporated into the subsequent probability inference formula. The main body is not judged in a vacuum, but based on... Historical accumulation and The judgment is based on real-time fluctuations; S1.3, Define cumulative experience and the number of participants in real time This allows us to characterize both "lower threshold with accumulation" in the cost function and "lower efficiency with congestion" in the correction term; S1.4, Define the capability status for the subject. And set the attention filtering threshold. Minimum acceptable profit threshold These thresholds will be used for tiered filtering and subsequent feedback triggering criteria; In step S2, the structured reading of public clues: S2.1, at time Constructing an observation set for the main body Furthermore, a moving average or time window can be added to the implementation layer (nearest). To reduce noise, the average value of each period is used. It does not fluctuate drastically due to accidental contributions, making it easy to perform stable probability calculations; S2.2, will Normalization or logarithmic compression (commonly used in implementation) However, in theoretical expression it is still based on Entering the likelihood form; S2.3, Building a long-standing reputation Current popularity Intrinsic attributes of the project The evidence is clearly defined as three channels, and all are uniformly incorporated into a Bayesian synthesis probability formula; S2.4, Subject-specific parameters As an exponential weight applied to and Up, and As a response Sensitivity coefficient; In step S3, the probabilistic inference of chance credibility: S3.1. In the absence of real-time information or during periods of information scarcity, an initial ranking can still be formed based on historical consensus and inherent attractiveness, yielding the following prior probabilities: When implementing, it can be... , Rewritten as k, k is used to express individual differences; S3.2. Interpreting "current public strength" as the strength of evidence for the hypothesis that "the project is indeed worth investing in," we use a likelihood function in the following form: in The amplification factor of the heat difference by the control subject; The larger the size, the easier it is for the main body to concentrate on a few hot spots in the early stages; the smaller the size, the more exploratory it retains. S3.3. Combine prior knowledge and likelihood to form "the strength of belief in the value of the project at the current moment." According to Bayes' theorem, this is achieved by observing evidence... Afterwards, the subject responded to the hypothesis. The belief is updated to the posterior probability: The purpose of this step is not to directly decide "to do it", but to compress the massive number of options into a set of probability weights, so that subsequent rational screening is computationally feasible. S3.4. Use a threshold to concentrate the subject's evaluation resources on a few alternatives, simulating attention allocation under information overload, and obtain the Bayesian posterior probability of each item. Subsequently, the innovation entities do not directly select randomly based on this probability, but first conduct a rational screening. A threshold for attention is set. A set of candidate projects worthy of further value assessment was selected: in The set of all innovative projects in the system, with a posterior probability threshold. The introduction of this means that the subject may miss those projects that have "weak signals but high actual value", which is the price of bounded rational decision-making: in an environment of information overload, the subject must sacrifice some exploration possibilities in exchange for decision-making efficiency. In step S4, the individualization of subjective value: S4.1, The objective attractiveness of the project Transforming this into "expected value from the perspective of a particular subject," we introduce cognitive differences: in For the project Its inherent appeal (objective value). as the main body For the project The subjective perception deviation coefficient; S4.2. To ensure that biases have a statistical structure rather than random noise, making them analyzable and simulable, this paper assumes that, in order to characterize reasonable differences in value judgments among individuals, the following assumptions are made. Follows a log-normal distribution: This setting has the following properties: ① Positiveness: ensuring value perception. ② Mean normalization: group average perception and project intrinsic value ③ Maintain consistency to avoid systematic overestimation or underestimation; ④ Controllable heterogeneity: variance From parameters Complete decision, The larger the value, the stronger the disagreement in judgments among subjects, which can flexibly simulate innovation environments with different degrees of heterogeneity; S4.3, Put Extending from a single scalar to an interpretable structure facilitates more specialized subsequent implementations, allowing for... It comes from a weighted average of multiple indicators (such as technical feasibility, industrial traction, and scientific significance). This can be decomposed into the product of capability mismatch bias and risk preference bias; however, it is ultimately converted back to... Enter your utility formula without compromising the mathematical backbone of the paper's model; S4.4 Clarify that "probability inference" only determines the focus of attention, while "value expectation" enters the profit calculation. Avoid confusing βk with εjk, and strictly distinguish between them: βk appears in... In the middle, represents the weight of ηj by the subject during the attention phase; while Direct change Impact on utility The size of the decision determines the logical "two-stage decision-making" process in the model: screening first, then calculation. In step S5, the dynamic characterization of the collaboration threshold: S5.1. Define a "starting point" collaboration cost benchmark for each project, as a reference for subsequent reductions. This is a comprehensive measure of the inherent complexity of a project and the initial resource threshold, so that different projects have different levels of difficulty even when there is zero experience. S5.2. Convert the historical accumulation of participating behaviors into state variables that can be invoked by the cost function: : the cutoff time Historically involved in projects The cumulative number of all independent entities, i.e., of which For a moment The number of real-time participants, a variable that represents the "stock of collective experience" accumulated by the project, is the basis for the generation of synergy. S5.3, Formulate the mechanism of "knowledge accumulation lowering the threshold for newcomers", the main body At any moment Participate in the project The required dynamic collaboration costs Defined as: in: : indicates project The baseline collaboration cost refers to the standardized cost that an innovator must pay to independently explore and attempt to solve the core problems of a project when there is no prior collaboration experience (i.e., zero experience stock). This value reflects the inherent technical complexity and initial resource threshold of the project. in ≥0 represents the synergistic effect coefficient; =0, then the cost is constant. If the value is greater than 0, the cost decreases power-lawically with the existing stock of experience. Here, +1 is used to prevent this. Sometimes meaningless divergence occurs; S5.4. Separate "long-term cost reduction" and "short-term congestion" at the variable level to ensure that the interpretation of the cost function is not contaminated when congestion effects are subsequently added. In this step, only allow... rely , without introducing And record in the data structure layer This allows for the use of utility correction terms in the next major step, thus maintaining the "reduce costs first, then correct congestion" modeling approach in your paper; In step S6, the optimal term for the formation of the input-output ratio is locked: S6.1 Unify value and cost into a "cost-effectiveness" indicator that can be compared across projects. After the entity forms value and cost estimates, it needs to conduct a cost-benefit analysis to form an overall incentive assessment for participating behavior, and define the entity. Participate in the project The unit cost value utility obtained for: The utility function It is the core intrinsic incentive variable driving the subject's subsequent feedback behavior. Its economic meaning is the expected return on investment per unit of cost incurred by the subject, which can be compared to a kind of "return on investment in innovation." A high value indicates that the subject believes (or experiences) the participation behavior has a high "cost-effectiveness," meaning the perceived value significantly outweighs the dynamic collaboration costs incurred, leading to strong satisfaction. Conversely, a low value indicates a lower value. A ratio implies that the expected value is less than the required cost, and participation may be seen as a losing proposition. The reason for using a ratio rather than a difference is that the benchmark collaboration cost varies across different projects. There may be differences in magnitude, making it difficult to compare the "cost-effectiveness" across projects, while the ratio form provides a dimensionless standardized measurement, which is more suitable for horizontal comparisons between different projects. S6.2 Eliminate obviously uneconomical options in advance to reduce noise in subsequent comparisons. The economic meaning of the participation threshold: when... When the expected value is greater than or equal to the participation cost ( The participation behavior has non-negative expected returns, satisfying the basic participation condition of the rational economic agent assumption: S6.3, to characterize the immediate nonlinearity of "too few people leading to insufficient collaboration, too many people leading to communication congestion," and to ensure that the choice reflects the actual state of collaboration, in order to bridge this theoretical gap and enable the model to simultaneously encompass both historical knowledge collaboration and immediate scale effects, the core utility function is optimized by introducing an immediate adjustment factor—the collaboration coefficient. This allows us to construct a more complete actual utility function: Newly added synergy coefficient Specifically designed to characterize the current scale of collaboration The immediate nonlinear impact on individual expected efficacy, with the addition of a synergy coefficient. Specifically designed to characterize the current scale of collaboration The immediate nonlinear impact on individual expected efficacy; S6.4. Transform the "comparison" into explicit action instructions, output the target projects that the entity should invest in during the current period, and select the project that brings the greatest unit cost value from the set of feasible projects to maximize profits: ; Step S7, the action grouping process, is being executed: S7.1 Ensure that "project selection" goes beyond mere intention, generating measurable input records, and at time t, the main body... With the selected project Binding, forming a participating set (i.e., projects participated in during the current period) The main body set) is registered, this step is for subsequent experience storage. The recursion and the scale coefficient The calculations provide a factual basis; S7.2, Specify the condition "whether collaboration is required". To link feedback to project progress and avoid distortion, updates should be made during implementation based on the current challenges of the project and the matching of core capabilities (available capability thresholds or skill vectors). If the main body can independently solve the core problem, then... =1, otherwise =0, this step directly determines whether it is allowed to make a positive contribution to the public strength in the future; S7.3. The cost function is guaranteed to use accumulated experience "up to the present" rather than a static constant, and is updated at the end of the period based on session participation. (Or use cumulative sum as you see fit), thus enabling the next period's... The changes reflect a structure where "the longer the collaboration lasts, the less effort the newcomer has." S7.4. In more specialized implementations, it is necessary to handle situations where the subject switches between projects across multiple cycles, and multiple projects simultaneously compete for the same subject's resources. Resource budgets (time / computing power / attention) should be set for the subject, and it should be constrained that a maximum of one project can be committed to per cycle, or allocated proportionally. Although your paper's main body uses single-choice... While simplified, the engineering implementation can avoid unrealistic "infinite parallel investment" through budget constraints without altering the core formula.

[0020] 9. The innovative resource self-organizing scheduling method based on ant colony collaboration and Bayesian learning according to claim 1, characterized in that, in step S8, the endogenous incremental decay of environmental memory: S8.1 Establish a monotonic mapping between the subject's subjective experience and the increment of public memory, so that public clues can reflect real value. Use the subject's current utility on the selected item as the strength benchmark. When contribution is allowed, the increment is proportional to the utility, providing a summable "contribution" for subsequent public strength updates. S8.2 Ensure that only entities that "still require collaboration and have a positive experience" will positively contribute to public intensity, preventing entities that "have completed / do not require collaboration" from continuing to create false buzz. After participating in a project, entities should conditionally release feedback signals to the environment based on their actual experience and problem-solving status. The rules are as follows: in, This indicates whether the entity possesses the ability to independently resolve the project's current key issues; This is a constant for signal release intensity. A reinforced signal proportional to utility will only be released when the subject needs cooperation and gains a positive experience (utility > 1); if the problem has been solved or the experience is average, it will remain silent. S8.3. Each subject is given a clear incremental calculation formula for easy aggregation. When the above triggering conditions are met, the incremental signal released by the subject is positively correlated with its perceived positive utility, and the functional relationship is defined as follows: in, As the signal release intensity constant, it scales the subjective utility of the subject to the objective contribution to the environmental signal; S8.4. Summarize the increments of all subjects at the project level and add a decay term to simulate attention shift and information obsolescence to avoid permanent locking. At the end of each cycle, perform the following for each project: first decay, then add the increment. At the project granularity, this can be written as: in As the decay rate, this formula unifies the "forgetting mechanism" and the "contribution mechanism" into a single recursive equation, which becomes the input for the next round of inference; In step S9, closed-loop iterative structural evolution control: S9.1 Ensure the system strictly iterates in the order of "observation → inference → screening → accounting → action → write-back" to avoid state referencing errors. Perform consistency checks at the loop boundaries: Updates must be completed at the same time level to ensure the next round Using the latest ; S9.2. In the early stages or when information is insufficient, to prevent the system from being locked into suboptimal items too early due to random fluctuations, a small probability random perturbation is added to the selection operator in the implementation, or time annealing is performed on the P threshold (initially a low threshold expands the candidate set, and later the threshold is increased to strengthen the focus). S9.3, When the scale exceeds the optimal collaboration scale At that time, the actual utility of the subject naturally decreases, thus in The emergence of alternative projects drives structural migration and increases the synergy coefficient. Specifically designed to characterize the current scale of collaboration The immediate nonlinear impact on individual expected efficacy, with the addition of a synergy coefficient. Specifically designed to characterize the current scale of collaboration The immediate nonlinear impact on individual expected efficacy is represented by the synergy coefficient function as follows: :project The current number of participants ( ), is a dynamic variable; :project The theoretical optimal scale of collaboration is determined by factors such as project complexity, task decomposability, and the level of collaboration tools. Coordination coefficient, range of values This represents the proportion of individual expected effectiveness achieved at the current scale; S9.4. Provide clear criteria for "when to stop / when to consider stable" for experimental reproduction and engineering deployment, and set termination conditions such as: reaching the maximum cycle time. or If the change is less than the threshold for L consecutive rounds, or if it participates in the distribution... KL divergence convergence, etc.; and define the output index caliber (e.g., per period). Individual Sequence, and The distribution of the data is used to analyze the interpretability and comparability of the "emergent structure" in subsequent analyses.

[0021] In this embodiment, the innovative resource self-organizing scheduling method based on ant colony collaboration and Bayesian learning effectively optimizes resource allocation and collaboration patterns by combining the decision-making and collaborative behaviors of individual innovative agents and utilizing dynamically updated public clues, opportunity credibility inference, subjective value assessment, and dynamic characterization of collaboration thresholds. In practical applications, Bayesian inference improves decision-making efficiency and reduces the negative impact of information overload. By introducing synergy effects and real-time adjustment mechanisms, it ensures efficient collaboration even when the project scale expands. Through feedback mechanisms and the endogenous decay of environmental memory, it enhances the system's adaptability and continuous optimization capabilities, thereby improving the utilization efficiency of innovative resources and the success rate of project execution.

[0022] Working principle like Figure 1-8 As shown, the innovation resource self-organization scheduling method based on ant colony collaboration and Bayesian learning initializes the element space state variables to ensure that the interaction between innovation subjects and projects has a clear starting point. Each subject (innovator) and project is given heterogeneous parameters and inherent attributes. The subject's decision-making basis not only depends on historical data, but also adjusts over time. The common strength, experience accumulation and capability status set by the system provide a basis for subsequent decision-making. These variables enter the subsequent probability inference. Combined with the Bayesian method, it ensures that the subject can make the optimal choice by fusing historical data and real-time information when information is scarce or incomplete.

[0023] In practical applications, Bayesian inference plays a crucial role. Each innovation agent comprehensively evaluates a project's value based on current public strength, long-term reputation, and inherent attractiveness. Through probabilistic inference of opportunity credibility, agents can update the posterior probability of projects according to the Bayesian formula, selecting the most promising projects. In cases of information overload, the system introduces a threshold mechanism to help agents focus on a few candidate projects, avoiding information interference during the decision-making process. The individualized generation of subjective value and the dynamic characterization of collaboration thresholds allow each agent to adjust their participation in decision-making based on their own cognitive biases, accumulated experience, and project characteristics, ensuring a more personalized and efficient decision-making process.

[0024] This method further optimizes project execution by introducing synergy and real-time adjustment mechanisms. Collaboration costs decrease with the accumulation of experience, enabling stakeholders to play a greater role in projects. It also addresses project expansion by adjusting collaboration thresholds and cost structures. Through feedback mechanisms and endogenous incremental decay of environmental memory, the system can adapt to dynamic changes and maintain high decision-making efficiency. Driven by economies of scale and knowledge accumulation, the system continuously optimizes the allocation of innovative resources, ensuring that collaboration can still be effectively executed in dynamic environments, thereby improving resource utilization and project success rate.

[0025] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An innovative resource self-organizing scheduling method based on ant colony collaboration and Bayesian learning, characterized in that, Includes the following steps: S1. Initialize the spatial state variables of the feature; S2, Structured reading of public clues; S3, Probabilistic Inference of Opportunity Credibility; S4. The individualized generation of subjective value; S5, Dynamic depiction of collaboration thresholds; S6. Optimal term for investment return ratio is locked in; S7. Execution of the action grouping process; S8. Endogenous incremental decay of environmental memory; S9, Closed-loop iterative structural evolution control.

2. The innovation resource self-organizing scheduling method based on ant colony cooperation and Bayesian learning according to claim 1, characterized in that: In step S1, during the initialization of feature space state variables: S1.1 Defining the set of innovation subjects With project collection And configure heterogeneous parameters for each subject ( Characterizes its weight for different information sources. (characterizing its sensitivity to public clues), while configuring inherent appeal for each project. Collaboration threshold with benchmarks This ensures that all subsequent decisions, learning, and interactions have a clear variable carrier; S1.2, define public intensity over time for each item and given historical baseline These quantities will go into the subsequent probability inference formula, the subject is not in a vacuum to judge, but based on The historical sedimentation of Real-time fluctuations form a judgment; S1.3, Define cumulative experience and the number of participants in real time This allows us to characterize both "lower threshold with accumulation" in the cost function and "lower efficiency with congestion" in the correction term; S1.4, Define the capability status for the subject. And set the attention filtering threshold. Minimum acceptable profit threshold These thresholds will be used for tiered filtering and subsequent feedback triggering criteria.

3. The innovative resource self-organizing scheduling method based on ant colony collaboration and Bayesian learning according to claim 1, characterized in that: In step S2, the structured reading of public clues: S2.1, at time Constructing an observation set for the main body Furthermore, a moving average or time window can be added to the implementation layer (nearest). To reduce noise, the average value of each period is used. It does not fluctuate drastically due to accidental contributions, making it easy to perform stable probability calculations; S2.2, will Normalization or logarithmic compression (commonly used in implementation) However, in theoretical expression it is still based on Entering the likelihood form; S2.3, Building on long-term reputation Current popularity Intrinsic attributes of the project The evidence is clearly defined as three channels, and all are uniformly incorporated into a Bayesian synthesis probability formula; S2.4, Subject-specific parameters As an exponential weight applied to and Up, and As a response The sensitivity coefficient.

4. The innovative resource self-organizing scheduling method based on ant colony collaboration and Bayesian learning according to claim 1, characterized in that: In step S3, the probabilistic inference of chance credibility: S3.

1. In the absence of real-time information or during periods of information scarcity, an initial ranking can still be formed based on historical consensus and inherent attractiveness, yielding the following prior probabilities: When implementing, you can... , Rewritten as k, k is used to express individual differences; S3.

2. Interpreting "current public strength" as the strength of evidence for the hypothesis that "the project is indeed worth investing in," we use a likelihood function in the following form: in The amplification factor of the heat difference by the control subject; The larger the size, the easier it is for the main body to concentrate on a few hot spots in the early stages; the smaller the size, the more exploratory it retains. S3.

3. Combine prior knowledge and likelihood to form "the strength of belief in the value of the project at the current moment." According to Bayes' theorem, this is achieved by observing evidence... Afterwards, the subject responded to the hypothesis. The belief is updated to the posterior probability: The purpose of this step is not to directly decide "to do it", but to compress the massive number of options into a set of probability weights, so that subsequent rational selection is computationally feasible; S3.

4. Use a threshold to concentrate the subject's evaluation resources on a few alternatives, simulating attention allocation under information overload, and obtain the Bayesian posterior probability of each item. Subsequently, the innovation entities do not directly select randomly based on this probability, but first conduct a rational screening. A threshold for attention is set. A set of candidate projects worthy of further value assessment was selected: in The set of all innovative projects in the system, with a posterior probability threshold. The introduction of this means that the subject may miss those projects that have "weak signals but high actual value," which is the price of bounded rational decision-making: in an environment of information overload, the subject must sacrifice some exploration possibilities in exchange for decision-making efficiency.

5. The innovative resource self-organizing scheduling method based on ant colony collaboration and Bayesian learning according to claim 1, characterized in that: In step S4, the individualization of subjective value: S4.1, The objective attractiveness of the project Transforming this into "the expected value from the perspective of a particular subject," we introduce cognitive differences: in For the project Its inherent appeal (objective value). as the main body For the project The subjective perception deviation coefficient; S4.

2. To ensure that biases have a statistical structure rather than random noise, making them analyzable and simulable, this paper assumes that, in order to characterize reasonable differences in value judgments among individuals, the following assumptions are made. Follows a log-normal distribution: This setting has the following properties: ① Positiveness: ensuring value perception. ② Mean normalization: group average perception and project intrinsic value ③ Maintain consistency to avoid systematic overestimation or underestimation; ④ Controllable heterogeneity: variance From parameters Complete decision, The larger the value, the stronger the disagreement in judgments among subjects, which can flexibly simulate innovation environments with different degrees of heterogeneity; S4.3, put Extending from a single scalar to an interpretable structure facilitates more specialized subsequent implementations, allowing for... It comes from a weighted average of multiple indicators (such as technical feasibility, industrial traction, and scientific significance). This can be decomposed into the product of capability mismatch bias and risk preference bias; however, it is ultimately converted back to... Enter your utility formula without compromising the mathematical backbone of the paper's model; S4.4 Clarify that "probability inference" only determines the focus of attention, while "value expectation" enters the profit calculation. Avoid confusing βk with εjk, and strictly distinguish between them: βk appears in... In the middle, represents the weight of ηj by the subject during the attention phase; while Direct change Impact on utility The size of the decision determines the logical "two-stage decision-making" process of the model: screening first, then calculation.

6. The innovative resource self-organizing scheduling method based on ant colony collaboration and Bayesian learning according to claim 1, characterized in that, In step S5, the dynamic characterization of the collaboration threshold: S5.

1. Define a "starting point" collaboration cost benchmark for each project as a reference for subsequent reductions. This is a comprehensive measure of the inherent complexity of a project and the initial resource threshold, so that different projects have different levels of difficulty even when there is zero experience. S5.

2. Convert the historical accumulation of participating behaviors into state variables that can be invoked by the cost function: : the cutoff time Historically involved in projects The cumulative number of all independent entities, i.e., of which For a moment The number of real-time participants, a variable that represents the "stock of collective experience" accumulated by the project, is the basis for the generation of synergy. S5.3, Formulate the mechanism of "knowledge accumulation lowering the threshold for newcomers", the main body At any moment Participate in the project The required dynamic collaboration costs Defined as: in: : indicates project The baseline collaboration cost refers to the standardized cost that an innovator must pay to independently explore and attempt to solve the core problems of a project when there is no prior collaboration experience (i.e., zero experience stock). This value reflects the inherent technical complexity and initial resource threshold of the project. in ≥0 represents the synergistic effect coefficient; =0, then the cost is constant. If the value is greater than 0, the cost decreases power-lawically with the existing stock of experience. Here, +1 is used to prevent this. Sometimes meaningless divergence occurs; S5.4 Separate "long-term cost reduction" and "short-term congestion" at the variable level to ensure that the interpretation of the cost function is not contaminated when congestion effects are added later. In this step, only allow... rely , without introducing And record in the data structure layer This allows for the use of utility correction terms in the next major step, thus maintaining the "reduce costs first, then correct congestion" modeling approach in your paper.

7. The innovative resource self-organizing scheduling method based on ant colony collaboration and Bayesian learning according to claim 1, characterized in that, In step S6, the optimal term for the formation of the input-output ratio is locked: S6.1 Unify value and cost into a "cost-effectiveness" indicator that can be compared across projects. After the entity forms value and cost estimates, it needs to conduct a cost-benefit analysis to form an overall incentive assessment for participating behavior, and define the entity. Participate in the project The unit cost value utility obtained for: The utility function It is the core intrinsic incentive variable driving the subject's subsequent feedback behavior. Its economic meaning is the expected return on investment for each unit of cost incurred by the subject, which can be compared to a kind of "return on investment in innovation." A high value indicates that the subject believes (or experiences) the participation behavior has a high "cost-effectiveness," meaning the perceived value significantly outweighs the dynamic collaboration costs incurred, leading to strong satisfaction. Conversely, a low value indicates a lower value. A ratio implies that the expected value is less than the required cost, and participation may be seen as a losing proposition. The reason for using a ratio rather than a difference is that the benchmark collaboration cost varies across different projects. There may be differences in magnitude, making it difficult to compare "cost-effectiveness" across projects using the difference form, while the ratio form provides a dimensionless standardized measurement, which is more suitable for making horizontal comparisons between different projects. S6.2 Eliminate obviously uneconomical options in advance to reduce noise in subsequent comparisons. The economic meaning of the participation threshold: when... When the expected value is greater than or equal to the participation cost ( The participation behavior has non-negative expected returns, satisfying the basic participation condition of the rational economic agent assumption: S6.3, to characterize the immediate nonlinearity of "too few people leading to insufficient collaboration, too many people leading to communication congestion," and to ensure that the choice reflects the actual state of collaboration, in order to bridge this theoretical gap and enable the model to simultaneously encompass both historical knowledge collaboration and immediate scale effects, the core utility function is optimized by introducing an immediate adjustment factor—the collaboration coefficient. This allows us to construct a more complete actual utility function: Newly added synergy coefficient Specifically designed to characterize the current scale of collaboration The immediate nonlinear impact on individual expected efficacy, with the addition of a synergy coefficient. Specifically designed to characterize the current scale of collaboration The immediate nonlinear impact on individual expected efficacy; S6.4 Transform "comparison" into explicit action instructions, and output the target items that the subject needs to invest in during the current period: Within a pool of feasible projects, the entity selects the project that delivers the highest unit cost value to maximize profits.

8. The innovative resource self-organizing scheduling method based on ant colony collaboration and Bayesian learning according to claim 1, characterized in that, Step S7, the action grouping process, is being executed: S7.1 Ensure that "project selection" goes beyond mere intention, generating measurable input records, and at time t, the subject... With the selected project Binding, forming a participating set (i.e., projects participated in during the current period) The main body set) is registered, this step is for subsequent experience storage. The recursion and the scale coefficient The calculations provide a factual basis; S7.2, Specify the condition "whether collaboration is required". To link feedback to project progress and avoid distortion, updates should be made during implementation based on the current challenges of the project and the matching of core capabilities (available capability thresholds or skill vectors). If the main body can independently solve the core problem, then... =1, otherwise =0, this step directly determines whether it is allowed to make a positive contribution to the public strength in the future; S7.

3. The cost function is guaranteed to use accumulated experience "up to the present" rather than a static constant, and is updated at the end of the period based on session participation. (Or use cumulative sum as you see fit), thus enabling the next period's... The changes reflect a structure where "the longer the collaboration lasts, the less effort the newcomer has." S7.

4. In more specialized implementations, it is necessary to handle situations where the subject switches between projects across multiple cycles, and multiple projects simultaneously compete for the same subject's resources. Resource budgets (time / computing power / attention) should be set for the subject, and it should be constrained that a maximum of one project can be committed to per cycle, or allocated proportionally. Although your paper's main body uses single-choice... While simplified, the engineering implementation can avoid unrealistic "infinite parallel investment" through budget constraints without altering the core formula.

9. The innovative resource self-organizing scheduling method based on ant colony collaboration and Bayesian learning according to claim 1, characterized in that, In step S8, the endogenous incremental decay of environmental memory: S8.1 Establish a monotonic mapping between the subject's subjective experience and the increment of public memory, so that public clues can reflect real value. Use the subject's current utility on the selected items as the strength benchmark. When contribution is allowed, the increment is proportional to the utility, providing a summable "contribution" for subsequent public strength updates. S8.2 Ensure that only entities that "still require collaboration and have a positive experience" will positively contribute to public intensity, preventing entities that "have completed / do not require collaboration" from continuing to create false buzz. After participating in a project, entities should conditionally release feedback signals to the environment based on their actual experience and problem-solving status. The rules are as follows: in, This indicates whether the entity possesses the ability to independently resolve the project's current key issues; This is a constant for signal release intensity. A reinforced signal proportional to utility will only be released when the subject needs cooperation and gains a positive experience (utility > 1); if the problem has been solved or the experience is average, it will remain silent. S8.

3. Each subject is given a clear incremental calculation formula for easy aggregation. When the above triggering conditions are met, the incremental signal released by the subject is positively correlated with its perceived positive utility, and the functional relationship is defined as follows: in, As the signal release intensity constant, it scales the subjective utility of the subject to the objective contribution to the environmental signal; S8.

4. Summarize the increments of all subjects at the project level and add a decay term to simulate attention shift and information obsolescence to avoid permanent locking. At the end of each cycle, perform the following for each project: first decay, then add the increment. At the project granularity, this can be written as: in As the decay rate, this formula unifies the "forgetting mechanism" and the "contribution mechanism" into a single recursive equation, which becomes the input for the next round of inference.

10. The innovative resource self-organizing scheduling method based on ant colony collaboration and Bayesian learning according to claim 1, characterized in that, In step S9, closed-loop iterative structural evolution control: S9.1 Ensure the system strictly iterates in the order of "observation → inference → screening → accounting → action → write-back" to avoid state referencing errors. Perform consistency checks at the loop boundaries: Updates must be completed at the same time level to ensure the next round Using the latest ; S9.

2. In the early stages or when information is insufficient, to prevent the system from being locked into suboptimal items too early due to random fluctuations, a small probability random perturbation is added to the selection operator in the implementation, or time annealing is performed on the P threshold (initially a low threshold expands the candidate set, and later the threshold is increased to strengthen the focus). S9.3, When the scale exceeds the optimal collaboration scale At that time, the actual utility of the subject naturally decreases, thus in The emergence of alternative projects drives structural migration and increases the synergy coefficient. Specifically designed to characterize the current scale of collaboration The immediate nonlinear impact on individual expected efficacy, with the addition of a synergy coefficient. Specifically designed to characterize the current scale of collaboration The immediate nonlinear impact on individual expected efficacy is represented by the synergy coefficient function as follows: :project The current number of participants (in real time) ), is a dynamic variable; :project The theoretical optimal scale of collaboration is determined by factors such as project complexity, task decomposability, and the level of collaboration tools. Coordination coefficient, range of values This represents the proportion of individual expected effectiveness achieved at the current scale; S9.

4. Provide clear criteria for "when to stop / when to consider stable" for experimental reproduction and engineering deployment, and set termination conditions such as: reaching the maximum cycle time. or If the change is less than the threshold for L consecutive rounds, or if it participates in the distribution... KL divergence convergence, etc.; and define the output index caliber (e.g., per period). Individual Sequence, and The distribution of the data is used to analyze the interpretability and comparability of the "emergent structure" in subsequent analyses.

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