Conflict mediation and negotiation rules generation method for rural tourism considering foreign capital and local villagers' appeal

Abstract

The application discloses a rural tourism conflict mediation and consultation rule generation method considering foreign capital and local villager demands, and through a multi-source heterogeneous data acquisition step, commercial demands such as investment return expectations of foreign capital and social demands such as land emotions of villagers are respectively acquired; an entropy weight method-TOPSIS model is used to map the heterogeneous demands to a four-dimensional evaluation system to generate a quantitative characteristic vector; a dynamic interest conflict graph is constructed by calculating vector distance to identify a core dispute area and a consensus area; Nash equilibrium and Shapley value algorithms are introduced to solve a game equilibrium point to generate initial consultation rules including voting weights, profit distribution mechanisms and decision-making processes; a multi-agent simulation environment is constructed to perform stress testing and parameter optimization on the rules, and a block chain is used for storage evidence.The application realizes quantitative alignment and incentive compatibility of heterogeneous demands, solves the problem of rigid rules in rural tourism development, effectively reduces the project trial and error cost, and guarantees the stability of cooperation.

Description

Technical Field

[0001] This invention relates to the field of smart rural governance technology, specifically to a method for generating rules of procedure for rural tourism conflict mediation and consultation that takes into account both the demands of external capital and local villagers. Background Technology

[0002] The "foreign capital + local resources" cooperation model is becoming increasingly popular, aiming to revitalize idle farmhouses, farmland, and ecological resources through capital injection. However, this cross-class cooperation is essentially an unequal power game: foreign capital usually controls funds, management, and market channels, pursuing the maximization of short-term investment returns; while local villagers control land and labor, focusing more on long-term survival and cultural dignity. Due to the fundamental differences in their objective functions, intense conflicts can easily erupt at key points such as land acquisition and demolition, profit distribution, and business models.

[0003] Currently, addressing conflicts of interest in rural tourism primarily relies on administrative intervention and traditional rules of procedure. These existing methods have significant limitations: The lag and one-sidedness of data processing: Existing mediation methods are mostly "firefighting after the fact," lacking a proactive risk warning mechanism. There is a lack of effective data collection and quantification methods for addressing villagers' implicit grievances and the implicit pressure from capital, resulting in decision-making often being based on fragmented, experience-based judgments. The incommensurability of heterogeneous demands: The "money demands" of capital and the "emotional / cultural demands" of villagers are heterogeneous data. Existing methods lack a unified mathematical conversion model, which makes it impossible to accurately measure the bottom line and red line of both parties in a multi-dimensional space, causing the negotiation process to degenerate into endless wrangling or unilateral crushing by the powerful party. The static rigidity of rule generation: Once legal provisions or village rules and regulations are established, they become stable and difficult to cope with the dynamic interest adjustment needs brought about by fluctuations in the tourism market (such as seasonality and sudden public health events), lacking the ability to adapt and evolve. At the academic research level, although some scholars have introduced game theory to analyze rural tourism conflicts, these studies mostly remain at the theoretical derivation stage and lack engineering implementation methods. At the engineering practice level, existing smart village systems mainly focus on security monitoring or e-commerce sales, completely lacking intelligent decision-making modules for "interpersonal conflict" and "rule generation." Specifically, existing technology has the following fatal flaws: Lack of simulation verification: Rulemakers cannot predict the effectiveness of a profit-sharing agreement or voting mechanism over the next five years, leading to "good intentions gone wrong"; Ignoring micro-game behavior: failing to consider the "free-rider" mentality among villagers or the "strategic concealment" by capital providers, resulting in the generated rules failing in reality due to the lack of incentive compatibility mechanisms; Limited technical means: It fails to effectively integrate natural language processing (NLP), multi-agent simulation (MAS), and game theory algorithms, and cannot provide an end-to-end automated solution from data acquisition to rule output; To this end, we propose a method for generating rules of procedure for rural tourism conflict mediation and consultation that takes into account both the demands of external capital and local villagers. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for generating rules of procedure for mediating and negotiating rural tourism conflicts that takes into account both the demands of external capital and local villagers, comprising the following steps: S1: Multi-source heterogeneous demand data collection and preprocessing steps: Obtain the business demand datasets from external capital parties from the data sources respectively. Data set of social demands of local villagers The dataset is then cleaned and structured. S2: Demand Quantification Mapping Steps: Construct a four-dimensional evaluation index system including economic, social, cultural, and ecological dimensions, and calculate the values ​​based on the entropy weight method-TOPSIS model. and Quantitative feature vector under the four-dimensional index system and ; S3: Conflict Map Construction Steps: Calculate the... and Based on the vector distances and deviation angles across various dimensions, a dynamic conflict of interest map is generated according to a pre-defined conflict intensity mapping table. ; S4: Rule generation and game-theoretic solution steps: The conflict graph... The pre-trained rule generation model is input, which uses the Nash equilibrium algorithm to solve for the mixed strategy equilibrium point between the capital side and the villagers side, and combines it with the Shapley value allocation model to generate a preliminary set of deliberation rules. ; S5: Simulation Verification and Adaptive Optimization Steps: Construct a multi-agent simulation environment containing multiple intelligent agents, and load the... Iterative evolution is performed based on the conflict resolution rate obtained from simulation feedback. Dynamically adjust rule parameters and output final rules of procedure. .

[0005] Preferably, the multi-source heterogeneous demand data collection and preprocessing steps in step S1 specifically include: S1.1: Capital Data Acquisition Sub-step: Connect to the company's ERP system or investment plan via API interface to extract data including investment amount. Expected annualized rate of return Investment recovery period Number of jobs created Structured data, including; S1.2: Sub-step for acquiring villager data: Unstructured text data from village affairs forums, social media group chats, and records are collected using web crawling technology. Named entity recognition is performed using the BERT-BiLSTM-CRF model to extract key entities and sentiment polarity scores related to land ownership, folk taboos, and environmental complaints. ; S1.3: Data Standardization Sub-step: Map all data to Min-Max normalization method. Intervals eliminate dimensional differences.

[0006] Preferably, the demand quantification mapping step in step S2 specifically includes: S2.1: Constructing a four-dimensional evaluation index system: Economic dimension This includes return on investment, ability to pay rent, and dividend payout ratio; Social dimension This includes the impact of local employment rates, infrastructure improvements, and income inequality. Cultural Dimension This includes tolerance for alterations to ancient buildings, the degree of preservation of traditional customs, and the level of cultural commercialization. Ecological dimension This includes sewage treatment capacity, compliance with ecological red lines, and investment in environmental restoration. S2.2: Feature Vector Generation: Determining the weights of each dimension using the entropy weight method. Calculate the weighted normalization matrix to obtain the capital side eigenvector. and villager feature vector .

[0007] Preferably, the conflict map construction step in step S3 specifically includes: S3.1: Conflict Intensity Calculation: Calculate the absolute value of the difference between capital and villagers in each dimension. And according to the formula Calculate the comprehensive conflict intensity index ,in ; S3.2: Map Generation: Conflict Intensity Dimensions greater than 0.7 are marked as "core disputed areas," while those less than or equal to 0.3 are marked as "core disputed areas." Dimensions ≤0.7 are marked as "negotiation game zone". Dimensions less than 0.3 are marked as "consensus zones," forming a visualized conflict map. .

[0008] Preferably, the rule generation and game-solving steps in step S4 specifically include: S4.1: Definition of Deliberative Entity Eligibility: The voting weight of capital parties is set according to the investment amount I. The voting weight of village representatives is set according to the number of registered residents and their land contribution rate. ; S4.2: Decision-making mechanism generation: If the issue falls within the "consensus zone," then "simplified procedure rules" are generated, requiring only a simple majority to pass; If the issue falls within the "negotiation and bargaining zone," then a "weighted voting rule" is generated, with a decision threshold value of [value missing]. ( , (This is the adjustment coefficient). If the issue falls within the "core dispute area," a "veto rule" will be generated, and an independent third-party arbitration mechanism will be mandatory. S4.3: Generation of Benefit Distribution Rules: Calculating the marginal contribution of the village collective in the cooperative game using the Shapley value formula. ,in accordance with Determine the guaranteed profit sharing ratio This ensures that villagers retain basic income even when they reach the point of capital loss.

[0009] Preferably, the simulation verification and adaptive optimization steps in step S5 specifically include: S5.1: Constructing a multi-agent simulation environment: Define four types of intelligent agents, namely, the capital agent. Village Representative Agent Village Committee Agent and environmental disturbance agents ; S5.2: Set the behavioral policy space: Set the policy set of each agent as {cooperate, compromise, be tough, withdraw}, and update the policy selection probability of each agent according to the reinforcement learning algorithm; S5.3: Monte Carlo Simulation: Run more than 1000 stochastic simulations to statistically determine the probability that the project will remain stable under external disturbances such as market fluctuations and policy changes. ; S5.4: Parameter fine-tuning: If If the threshold is less than 85%, the rules will be adjusted backtrackingly using gradient descent. The dividend ratio or voting weight parameters in the data, until... Meets standards, output .

[0010] Preferably, after step S5, the method further includes: S6: Blockchain Notarization Steps: The generated... The code is converted into smart contract code, deployed on a consortium blockchain, and uses the immutability of blockchain to lock the rules of procedure and record the results of each subsequent vote and execution.

[0011] A system for generating rules of procedure for resolving conflicts and negotiating disputes in rural tourism that takes into account both the demands of external capital and local villagers includes: The data acquisition module is used to perform step S1 in claim 1; A quantization calculation module is used to perform step S2 in claim 1; The map construction module is used to perform step S3 in claim 1; The rules engine module is used to execute step S4 in claim 1; The simulation optimization module is used to execute step S5 in claim 1.

[0012] Compared with existing technologies, this invention provides a method for generating rules of procedure for rural tourism conflict mediation and negotiation that takes into account both the demands of external capital and local villagers, and has the following beneficial effects: 1. This method for generating rules of procedure for rural tourism conflict mediation and negotiation, which takes into account the demands of both external capital and local villagers, solves the technical challenges of "language barriers and differing standards" between them. By constructing a four-dimensional evaluation index system and utilizing the entropy weight method-TOPSIS model, this invention, for the first time, maps economic indicators such as "return on investment and cash flow" (focused on by investors) and social indicators such as "cultural dignity and emotional belonging" (focused on by villagers) onto the same mathematical coordinate system for standardized processing. This vectorized approach to handling demands transforms previously vague and emotional arguments (such as "you can't touch that ancient tree") into precise and calculable quantitative parameters (such as "conflict intensity value F=0.92 for cultural protection dimension"). This not only eliminates information asymmetry between the two parties but also provides mediators with objective data dashboards, forcing both sides to return to rational negotiation and fundamentally avoiding irrational confrontations caused by misunderstandings.

[0013] 2. This method for generating rules for resolving conflicts and negotiating rural tourism disputes, which balances the demands of external capital and local villagers, differs from the traditional "one-size-fits-all" administrative orders. This invention introduces Nash equilibrium and Shapley value algorithms, achieving a shift from "externally imposed" to "endogenously generated" rules. Specifically, the system does not merely generate a rigid clause, but calculates an incentive-compatible mechanism: in terms of profit distribution, it ensures that while the capital party obtains its expected profit, the villagers collective receive a guaranteed minimum dividend commensurate with their marginal contribution; in terms of the decision-making mechanism, it automatically assigns different voting weights (such as veto power vs. simple majority rule) to issues of varying conflict intensity (core dispute area vs. consensus area). This game theory-based rule generation method ensures that regardless of market fluctuations, neither party can unilaterally profit by harming the other's interests, thus achieving Pareto optimality at the technical level and significantly improving the agreement fulfillment rate.

[0014] 3. This method for generating rural tourism conflict mediation and negotiation rules, which balances the demands of external capital and local villagers, breaks through the limitations of existing rule-making that "only considers the present and ignores the future." It constructs a virtual "digital village" through multi-agent simulation technology. Before the rules are officially implemented, the system can simulate thousands of potential extreme scenarios (such as the off-season for tourism, public health emergencies, and changes in core village leaders) to test the robustness of the generated rules. This "simulation first, execution later" model is equivalent to a low-cost stress test, enabling the early detection of loopholes in the rules (such as the risk of capital cash flow disruption and the risk of intensified wealth disparity among villagers), and automatically patching the rule parameters through a backpropagation algorithm. This not only significantly reduces the trial-and-error costs and legal risks of the project but also shifts rural governance from "passive firefighting" to "proactive fire prevention," significantly improving the stability of rural tourism projects throughout their entire lifecycle. Detailed Implementation

[0015] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example An Example of a Method for Generating Rules of Procedure for Conflict Mediation and Negotiation in Rural Tourism that Balances the Demands of External Capital and Local Villagers A method for generating rules of procedure for conflict mediation and negotiation in rural tourism that takes into account both the demands of external capital and local villagers includes the following steps: S1: Multi-source heterogeneous demand data collection and preprocessing steps: Obtain the business demand datasets from external capital parties from the data sources respectively. Data set of social demands of local villagers The dataset is then cleaned and structured. S2: Demand Quantification Mapping Steps: Construct a four-dimensional evaluation index system including economic, social, cultural, and ecological dimensions, and calculate the values ​​based on the entropy weight method-TOPSIS model. and Quantitative feature vector under the four-dimensional index system and ; S3: Conflict Map Construction Steps: Calculate the... and Based on the vector distances and deviation angles across various dimensions, a dynamic conflict of interest map is generated according to a pre-defined conflict intensity mapping table. ; S4: Rule generation and game-theoretic solution steps: The conflict graph... The pre-trained rule generation model is input, which uses the Nash equilibrium algorithm to solve for the mixed strategy equilibrium point between the capital side and the villagers side, and combines it with the Shapley value allocation model to generate a preliminary set of deliberation rules. ; S5: Simulation Verification and Adaptive Optimization Steps: Construct a multi-agent simulation environment containing multiple agents, and load... Iterative evolution is performed based on the conflict resolution rate obtained from simulation feedback. Dynamically adjust rule parameters and output final rules of procedure. .

[0017] Specifically, the multi-source heterogeneous demand data collection and preprocessing steps in step S1 include: S1.1: Capital Data Acquisition Sub-step: Connect to the company's ERP system or investment plan via API interface to extract data including investment amount. Expected annualized rate of return Investment recovery period Number of jobs created Structured data, including; S1.2: Sub-step for acquiring villager data: Unstructured text data from village affairs forums, social media group chats, and records are collected using web crawling technology. Named entity recognition is performed using the BERT-BiLSTM-CRF model to extract key entities and sentiment polarity scores related to land ownership, folk taboos, and environmental complaints. ; S1.3: Data Standardization Sub-step: Map all data to Min-Max normalization method. Intervals eliminate dimensional differences.

[0018] Specifically, the demand quantification and mapping step in step S2 includes: S2.1: Constructing a four-dimensional evaluation index system: Economic dimension This includes return on investment, ability to pay rent, and dividend payout ratio; Social dimension This includes the impact of local employment rates, infrastructure improvements, and income inequality. Cultural Dimension This includes tolerance for alterations to ancient buildings, the degree of preservation of traditional customs, and the level of cultural commercialization. Ecological dimension This includes sewage treatment capacity, compliance with ecological red lines, and investment in environmental restoration. S2.2: Feature Vector Generation: Determining the weights of each dimension using the entropy weight method. Calculate the weighted normalization matrix to obtain the capital side eigenvector. and villager feature vector .

[0019] Specifically, the conflict map construction step in step S3 includes: S3.1: Conflict Intensity Calculation: Calculate the absolute value of the difference between capital and villagers in each dimension. And according to the formula Calculate the comprehensive conflict intensity index ,in ; S3.2: Map Generation: Conflict Intensity Dimensions greater than 0.7 are marked as "core disputed areas," while those less than or equal to 0.3 are marked as "core disputed areas." Dimensions ≤0.7 are marked as "negotiation game zone". Dimensions less than 0.3 are marked as "consensus zones," forming a visualized conflict map. .

[0020] Specifically, step S4, which involves rule generation and game solving, includes: S4.1: Definition of Deliberative Entity Eligibility: The voting weight of capital parties is set according to the investment amount I. The voting weight of village representatives is set according to the number of registered residents and their land contribution rate. ; S4.2: Decision-making mechanism generation: If the issue falls within the "consensus zone," then "simplified procedure rules" are generated, requiring only a simple majority to pass; If the issue falls within the "negotiation and bargaining zone," then a "weighted voting rule" is generated, with a decision threshold value of [value missing]. ( , (This is the adjustment coefficient). If the issue falls within the "core dispute area," a "veto rule" will be generated, and an independent third-party arbitration mechanism will be mandatory. S4.3: Generation of Benefit Distribution Rules: Calculating the marginal contribution of the village collective in the cooperative game using the Shapley value formula. ,in accordance with Determine the guaranteed profit sharing ratio This ensures that villagers retain basic income even when they reach the point of capital loss.

[0021] Specifically, the simulation verification and adaptive optimization steps in step S5 include: S5.1: Constructing a multi-agent simulation environment: Define four types of intelligent agents, namely, the capital agent. Village Representative Agent Village Committee Agent and environmental disturbance agents ; S5.2: Set the behavioral policy space: Set the policy set of each agent as {cooperate, compromise, be tough, withdraw}, and update the policy selection probability of each agent according to the reinforcement learning algorithm; S5.3: Monte Carlo Simulation: Run more than 1000 stochastic simulations to statistically determine the probability that the project will remain stable under external disturbances such as market fluctuations and policy changes. ; S5.4: Parameter fine-tuning: If If the threshold is less than 85%, the rules will be adjusted backtrackingly using gradient descent. The dividend ratio or voting weight parameters in the data, until... Meets standards, output .

[0022] Specifically, after step S5, the following also includes: S6: Blockchain Notarization Steps: The generated... The code is converted into smart contract code, deployed on a consortium blockchain, and uses the immutability of blockchain to lock the rules of procedure and record the results of each subsequent vote and execution.

[0023] A system for generating rules of procedure for resolving conflicts and negotiating disputes in rural tourism that takes into account both the demands of external capital and local villagers includes: The data acquisition module is used to perform step S1 in claim 1; A quantization calculation module is used to perform step S2 in claim 1; The map construction module is used to perform step S3 in claim 1; The rules engine module is used to execute step S4 in claim 1; The simulation optimization module is used to execute step S5 in claim 1.

[0024] Through the aforementioned technical solution, this invention addresses the technical challenge of "language barriers and differing standards" between external capital and local villagers. By constructing a four-dimensional evaluation index system and utilizing the entropy weight method-TOPSIS model, this invention, for the first time, maps economic indicators such as "return on investment and cash flow" (focused on by capital) and social indicators such as "cultural dignity and emotional belonging" (focused on by villagers) onto the same mathematical coordinate system for standardized processing. This vectorized approach to addressing demands transforms previously vague and emotional arguments (such as "You can't touch that ancient tree") into precise and calculable quantitative parameters (such as "conflict intensity value F=0.92 for cultural protection dimension"). This not only eliminates information asymmetry between the two parties but also provides mediators with objective data, forcing both sides to return to rational negotiation and fundamentally avoiding irrational confrontation caused by misunderstandings. Unlike traditional administrative command-style "one-size-fits-all" rules, this invention introduces Nash equilibrium and Shapley value algorithms, achieving a shift from "externally imposed" to "endogenously generated." Specifically, the system doesn't just generate rigid terms; it calculates an incentive-compatible mechanism: in profit distribution, it ensures that while investors receive their expected profits, the village collective receives a guaranteed dividend commensurate with their marginal contribution; in decision-making, it automatically assigns different voting weights (e.g., veto power vs. simple majority) to issues of varying conflict intensity (core dispute area vs. consensus area). This game theory-based rule generation method guarantees that regardless of market fluctuations, neither party can unilaterally profit by harming the other's interests, thus achieving Pareto optimality at the technical level. This significantly improves the agreement's compliance rate and overcomes the limitations of existing rule-making that "only considers the present and ignores the future." It constructs a virtual "digital village" through multi-agent simulation technology. Before the rules are officially implemented, the system can simulate thousands of potential extreme scenarios (such as the off-season for tourism, public health emergencies, and changes in core village leaders) to test the robustness of the generated rules of procedure. This "simulation first, execution later" model is equivalent to conducting a low-cost stress test, which can identify loopholes in the rules in advance (such as the risk of capital cash flow disruption and the risk of intensified wealth disparity among villagers), and automatically repair the rule parameters through backpropagation algorithms. This not only significantly reduces the trial-and-error costs and legal risks of the project, but also pushes rural governance from "passive firefighting" to "proactive fire prevention," significantly improving the stability of rural tourism projects throughout their entire life cycle.

[0025] 1. Data Acquisition and Preprocessing 1.1 Data Acquisition by Investors: The system connects to the investor's project management database using ETL tools to extract structured data. Financial Indicators: Total Investment 10,000 yuan, expected Investment recovery period Year.

[0026] Operational metrics: Number of planned old houses occupied The building is expected to receive high-end customers annually. people.

[0027] Constraints: Requires a 20-year exclusive operating right and a permit for modernizing the appearance of buildings in the core area.

[0028] 1.2 Villager Data Get: The system deploys a distributed crawler to capture data from villagers' WeChat groups, local Douyin (TikTok) accounts, and 12345 hotline records for NLP processing. Entity extraction: Identify key entities, such as "old camphor tree (location: village entrance)", "Li family ancestral hall", and "sewage pipe".

[0029] Sentiment Analysis: Sentiment polarity was calculated using the SnowNLP model. Results showed that the term "dividend" appeared most frequently, resulting in a high sentiment score. (Slightly negative, considered insufficient); "Old Camphor Tree" emotional score (Strongly oppose removal).

[0030] 1.3 Data Standardization: Construct the original matrix Rows represent the main entity (capital / villagers), and columns represent indicators. Z-score standardization is used to eliminate the influence of dimensions and generate a standardized matrix. .

[0031] 2. Quantitative Mapping of Demands 2.1 Weighting of the Four-Dimensional Indicator System: The system uses the entropy weighting method to calculate the weights of each dimension. Since the villager data shows the greatest variation (variance) in the "culture" dimension, the system automatically assigns a higher weight to this dimension.

[0032] Calculation results: .

[0033] 2.2 Feature Vector Generation (TOPSIS Model): Calculate the distance between the positive and negative ideal solutions.

[0034] Capital eigenvector It is extremely close to the positive ideal solution in the economic dimension, but far from the negative ideal solution in the cultural dimension.

[0035]

[0036] Villager feature vector It is extremely close to the ideal solution in the cultural dimension, and has a low requirement for investment scale in the economic dimension.

[0037]

[0038] 3. Conflict Map Construction 3.1 Calculate the intensity of the conflict: The conflict values ​​for each dimension are calculated using a combination of cosine similarity and Euclidean distance. .

[0039] Economic dimension: |0.95-0.40|=0.55 (moderate conflict) Cultural Dimension: |0.65-0.98|=0.33 (High Conflict, Core Controversy) 3.2 Atlas Generation: The system generates a "four-quadrant conflict map": Quadrant 1 (Core Controversial Area): Removal of old camphor trees and use of modern materials for exterior walls. Marked as a red alert, requiring a veto.

[0040] Second quadrant (negotiated game area): Ticket revenue sharing ratio, parking lot construction location. Marked in yellow for warning, requiring weighted voting.

[0041] Quadrants 3 and 4 (Consensus Zone): Garbage collection and road paving. Marked as green for passage, following simplified procedures.

[0042] 4. Rule generation and game theory solution 4.1 Deliberative Entities and Voting Weights: Capital side weight .

[0043] Villager Weighting: Each household has a base weight of 1. Additional weights are added if land leasing is involved. Total Villager Weighting .

[0044] Rule generation: To prevent "capital hegemony," it is stipulated that any single expenditure exceeding 5 million must be approved. More than 60% of the respondents agreed.

[0045] 4.2 Sharpe ratio benefit distribution: The system calculates the marginal contribution of the village collective in the cooperation. Assume total revenue... 10,000, capital gains alone Ten thousand, villagers' individual income .

[0046] Using the Shapley value formula, the reasonable distribution ratio that the village collective should receive is calculated as follows: .

[0047] The generation rule clause states: "After deducting the statutory reserve fund, no less than 25% of the project's net profit must be distributed to the village collective as dividends, of which 10% shall be used as a cultural protection fund." 5. Simulation Verification and Optimization 5.1 Multi-Agent Simulation Environment Setup: Building a simulation model using Python's Mesa framework: Agents: CapitalistAgent: Strategy set {Increase investment, reduce costs, withdraw capital}.

[0048] VillagerAgent: Strategy set {cooperate with operations, slack off}.

[0049] GovernmentAgent: Strategy set {regulation, subsidies, penalties}.

[0050] Environmental disturbances: Introducing random variables The simulation depicted a "flu pandemic" that occurred in the third year, resulting in a 70% drop in tourist numbers.

[0051] 5.2 Stress Testing and Parameter Backtracking: Initial results: In the third year, due to declining customer traffic, the probability of CapitalistAgent choosing the "withdrawal" strategy was as high as 90%, causing the system to crash.

[0052] Dynamic adjustment: The system automatically triggers the circuit breaker mechanism and modifies the rule terms. Original rule: Fixed annual dividends.

[0053] Optimized rules: Introduce a variable revenue clause. When annual visitor traffic falls below a threshold, investors can apply for deferred dividend payments, but must pledge project equity as collateral to compensate villagers.

[0054] Final result: After 1000 Monte Carlo simulations, the optimized rules increased the cooperative survival rate from 10% to 88%.

[0055] 6. Blockchain-based evidence storage Finally, the system compiles the generated "Rules of Procedure", "Dividend Algorithm", and "Voting Rights Configuration" into Solidity smart contracts and deploys them on the Tencent Cloud TBaaS (TrustSQL) consortium blockchain.

[0056] Technical effect: Every dividend distribution and every villager vote result is recorded on the blockchain in real time. Capital investors cannot tamper with the accounts, and villagers cannot make unreasonable repeated claims, achieving ultimate trust through "code is law."

[0057] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for generating rules of procedure for rural tourism conflict mediation and negotiation that takes into account both the demands of external capital and local villagers, characterized by: Includes the following steps: S1: Multi-source heterogeneous demand data collection and preprocessing steps: Obtain the business demand datasets from external capital providers from the data sources respectively. Data set of social demands of local villagers The dataset is then cleaned and structured. S2: Demand Quantification Mapping Steps: Construct a four-dimensional evaluation index system including economic, social, cultural, and ecological dimensions, and calculate the values ​​based on the entropy weight method-TOPSIS model. and Quantitative feature vector under the four-dimensional index system and ; S3: Conflict Map Construction Steps: Calculate the... and Based on the vector distances and deviation angles across various dimensions, a dynamic conflict of interest map is generated according to a pre-defined conflict intensity mapping table. ; S4: Rule generation and game-theoretic solution steps: The conflict graph... The pre-trained rule generation model is input, which uses the Nash equilibrium algorithm to solve for the mixed strategy equilibrium point between the capital side and the villagers side, and combines it with the Shapley value allocation model to generate a preliminary set of deliberation rules. ; S5: Simulation Verification and Adaptive Optimization Steps: Construct a multi-agent simulation environment containing multiple intelligent agents, and load the... Iterative evolution is performed based on the conflict resolution rate obtained from simulation feedback. Dynamically adjust rule parameters and output final rules of procedure. .

2. The method for generating rural tourism conflict mediation and negotiation rules that takes into account both external capital and local villagers' demands, as described in claim 1, is characterized in that: The multi-source heterogeneous demand data collection and preprocessing steps described in step S1 specifically include: S1.1: Capital Data Acquisition Sub-step: Connect to the company's ERP system or investment plan via API interface to extract data including investment amount. Expected annualized rate of return Investment recovery period Number of jobs created Structured data, including; S1.2: Sub-step for acquiring villager data: Unstructured text data from village affairs forums, social media group chats, and records are collected using web crawling technology. Named entity recognition is performed using the BERT-BiLSTM-CRF model to extract key entities and sentiment polarity scores related to land ownership, folk taboos, and environmental complaints. ; S1.3: Data Standardization Sub-step: Map all data to Min-Max normalization method. Intervals eliminate dimensional differences.

3. The method for generating rural tourism conflict mediation and negotiation rules that takes into account both external capital and local villagers' demands, as described in claim 1, is characterized in that: The demand quantification mapping step described in step S2 specifically includes: S2.1: Constructing a four-dimensional evaluation index system: Economic dimension This includes return on investment, ability to pay rent, and dividend payout ratio; Social dimension This includes factors such as local employment rate, infrastructure improvement, and the impact of income inequality. Cultural Dimension This includes tolerance for alterations to ancient buildings, the degree of preservation of traditional customs, and the level of cultural commercialization. Ecological dimension This includes sewage treatment capacity, compliance with ecological red lines, and investment in environmental restoration. S2.2: Feature Vector Generation: Determining the weights of each dimension using the entropy weight method. Calculate the weighted normalization matrix to obtain the capital side eigenvector. and villager feature vectors .

4. The method for generating rural tourism conflict mediation and negotiation rules that takes into account both external capital and local villagers' demands, as described in claim 1, is characterized in that: The conflict map construction step in step S3 specifically includes: S3.1: Conflict Intensity Calculation: Calculate the absolute value of the difference between capital and villagers in each dimension. And according to the formula Calculate the comprehensive conflict intensity index ,in ; S3.2: Map Generation: Conflict Intensity Dimensions greater than 0.7 are marked as "core disputed areas," and those less than or equal to 0.3 are marked as "core disputed areas." Dimensions ≤0.7 are marked as "negotiation game zone". Dimensions less than 0.3 are marked as "consensus zones," forming a visualized conflict map. .

5. The method for generating rural tourism conflict mediation and negotiation rules that takes into account both external capital and local villagers' demands, as described in claim 1, is characterized in that: The rule generation and game-solving steps in step S4 specifically include: S4.1: Definition of Deliberative Entity Eligibility: The voting weight of capital parties is set according to the investment amount I. The voting weight of village representatives is set according to the number of registered residents and their land contribution rate. ; S4.2: Decision-making mechanism generation: If the issue falls within the "consensus zone," then "simplified procedure rules" are generated, requiring only a simple majority to pass. If the issue falls within the "negotiation and bargaining zone," then a "weighted voting rule" is generated, with a decision threshold value of [value missing]. ( , (This is the adjustment coefficient). If the issue falls within the "core dispute area," a "veto rule" will be generated, and an independent third-party arbitration mechanism will be mandatory. S4.3: Generation of Benefit Distribution Rules: Calculating the marginal contribution of the village collective in the cooperative game using the Shapley value formula. ,in accordance with Determine the guaranteed profit sharing ratio This ensures that villagers retain basic income even when they reach the point of capital loss.

6. The method for generating rural tourism conflict mediation and negotiation rules that takes into account both external capital and local villagers' demands, as described in claim 1, is characterized in that: The simulation verification and adaptive optimization steps described in step S5 specifically include: S5.1: Constructing a multi-agent simulation environment: Define four types of intelligent agents, namely, the capital agent. Village Representative Agent Village Committee Agent and environmental disturbance agents ; S5.2: Set the behavioral policy space: Set the policy set of each agent as {cooperate, compromise, be tough, withdraw}, and update the policy selection probability of each agent according to the reinforcement learning algorithm; S5.3: Monte Carlo Simulation: Run more than 1000 stochastic simulations to statistically determine the probability that the project will remain stable under external disturbances such as market fluctuations and policy changes. ; S5.4: Parameter fine-tuning: If If the threshold is less than 85%, the rules will be adjusted backtrackingly using gradient descent. The dividend ratio or voting weight parameters in the data, until... Meets standards, output .

7. The method for generating rural tourism conflict mediation and negotiation rules that takes into account both external capital and local villagers' demands, as described in claim 1, is characterized in that: Following step S5, the following is also included: S6: Blockchain Notarization Steps: The generated... The code is converted into smart contract code, deployed on a consortium blockchain, and uses the immutability of blockchain to lock the rules of procedure and record the results of each subsequent vote and execution.

8. A system for generating rules of procedure for rural tourism conflict mediation and negotiation that takes into account both the demands of external capital and local villagers, characterized by: include: The data acquisition module is used to perform step S1 in claim 1; A quantization calculation module is used to perform step S2 in claim 1; The map construction module is used to perform step S3 in claim 1; The rules engine module is used to execute step S4 in claim 1; The simulation optimization module is used to execute step S5 in claim 1.